Nexdata provides high-quality Annotated Imagery Data annotation for bounding box, polygon,segmentation,polyline, key points,image classification and image description. We have handled tons of data for autonomous driving, internet entertainment, retail, surveillance and security and etc.

The booming Data Annotation & Collection Services market is projected to reach $75 Billion by 2033, fueled by AI adoption in autonomous driving, healthcare, and finance. Explore market trends, key players (Appen, Amazon, Google), and regional growth in this comprehensive analysis.

Surveillance Images for Person Detection

Subtitle: Annotated Surveillance Scenes for Person Detection

Description

This dataset was sourced from Roboflow and contains surveillance images annotated for detecting the presence of persons. Each image has an associated YOLO-format label file indicating whether a person is present. If no person is found in the image, the label file is empty.

Key Statistics (based on EDA): - Total Images: 6603
- Images with Person: 5265
- Images without Person: 1338
- Total Annotations (Occurrences): 12915

Below is an example chart illustrating these metrics:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7525078%2Ff99c507fc5abe5049ebf52a5147658ea%2Fdataset_metrics.png?generation=1740068365301814&alt=media" alt="Dataset Metrics">

Note: This dataset contains only the training set. For practical use, you should manually split the images into training, validation, and test sets (e.g., using a stratified approach) to ensure consistent class distribution.

Example Images

1. Image with Person
   https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7525078%2F5c2c2d81a14d377f7a1820db2713b933%2Fperson.png?generation=1740068434171388&alt=media" alt="Example with Person">
2. Image without Person
   https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7525078%2F42beec0552f579122ba7b9f0e2acc6a4%2Fno_person.png?generation=1740068481987096&alt=media" alt="Example without Person">

File Information

• Each image is stored in .jpg format.
• A corresponding .txt file in YOLO format is provided for each image.
• If an image does not contain a person, its label file is empty.
• You can integrate these pairs (image + label) into your pipeline and perform your own data splits (train/val/test).

Usage

1. Add or download this dataset to your notebook.
2. Split the images into training, validation, and test sets, if needed.
3. Train a YOLO model (or any object detection algorithm) using these images and labels.
4. Evaluate performance by monitoring metrics like mAP, precision, and recall.

Potential Applications

• Real-time surveillance systems
• Access control in restricted areas
• Research in computer vision for person detection

License & Acknowledgments

• License: Refer to Roboflow for the original license details.
• Credits: This dataset is made available for research and development in security and surveillance.
• Note: Always check the source (Roboflow) for any additional usage terms, especially for commercial or public distribution.

Snapshot img

AI In Video Surveillance Market Size 2025-2029

The AI in video surveillance market size is forecast to increase by USD 10.9 billion at a CAGR of 22.7% between 2024 and 2029.

The market is driven by escalating concerns for public safety and security, making it an essential technology for various industries and applications. The proliferation of edge AI computing further enhances the market's potential by enabling real-time analysis and faster response times. However, this market faces significant challenges. Pervasive privacy concerns necessitate stringent regulations and compliance measures, adding complexity to the landscape. Data security and privacy remain paramount, with cloud computing and edge computing solutions offering secure alternative
The regulatory environment remains fragmented, with varying rules and standards across regions, further complicating market penetration. Companies seeking to capitalize on this market must navigate these challenges effectively, ensuring data privacy and regulatory compliance while delivering advanced AI capabilities for enhanced security and safety solutions. The integration of natural language processing and cloud computing is further expanding the capabilities of robots, enabling them to interact with humans more effectively and process vast amounts of data in real-time.

What will be the Size of the AI In Video Surveillance Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market for AI in video surveillance continues to evolve, with advancements in image processing techniques, security camera calibration, and video analytics software driving innovation. Privacy enhancing technologies, such as facial recognition software, are increasingly integrated into CCTV camera systems to enhance risk assessment and alert notification capabilities. Real-time security alerts are generated through machine learning algorithms and activity recognition models, enabling proactive security measures and automated incident reporting. Video streaming protocols and visual search technology facilitate remote video monitoring and multi-camera tracking systems, providing intelligent video insights through data visualization tools.

Industry growth is expected to reach 15% annually, with companies investing in video data annotation, video quality enhancement, and data security measures to improve system performance and user experience. For instance, a leading retailer reported a 30% increase in sales due to the implementation of AI-powered video analytics applications.

How is this AI In Video Surveillance Industry segmented?

The AI in video surveillance industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Solution

  Hardware
  Software
  Services


Deployment

  Cloud based
  On premises


End-user

  Government and public facilities
  Commercial
  Military and defense
  Residential


Usage

  Intrusion detection
  Facial recognition
  Traffic monitoring
  Crowd management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Solution Insights

The Hardware segment is estimated to witness significant growth during the forecast period. The market is experiencing significant advancements, with the hardware segment witnessing a shift towards decentralized processing. This transition, seen in cameras, network video recorders, and dedicated edge appliances, addresses the need for real-time analytics, reduced network bandwidth usage, and heightened data privacy. The driving force behind this trend is the progression in system-on-chip (SoC) technology, such as Axis Communications AB's ARTPEC-8 SoC, which embeds a deep learning processing unit for advanced AI-based object analysis directly on the device. Intelligent video analytics, real-time threat assessment, and behavioral analytics are increasingly integrated into security systems. Lossless video compression, motion detection sensitivity, and object detection algorithms are optimizing video surveillance.

Neural network architecture and deep learning models power predictive policing tools and facial recognition accuracy. Data encryption protocols and anomaly detection systems ensure data security. Real-time video processing, video content analysis, and cloud-based video storage facilitate efficient management and access to video data. Crowd density estimation, event detection algorithms, and access control integration enhance security and operational efficiency. Intrusion detection technology and ed

Computer Vision Annotation Tool Market Outlook

According to our latest research, the global Computer Vision Annotation Tool market size reached USD 2.16 billion in 2024, and it is expected to grow at a robust CAGR of 16.8% from 2025 to 2033. By 2033, the market is forecasted to achieve a value of USD 9.28 billion, driven by the rising adoption of artificial intelligence and machine learning applications across diverse industries. The proliferation of computer vision technologies in sectors such as automotive, healthcare, retail, and robotics is a key growth factor, as organizations increasingly require high-quality annotated datasets to train and deploy advanced AI models.

The growth of the Computer Vision Annotation Tool market is primarily propelled by the surging demand for data annotation solutions that facilitate the development of accurate and reliable machine learning algorithms. As enterprises accelerate their digital transformation journeys, the need for precise labeling of images, videos, and other multimedia content has intensified. This is especially true for industries like autonomous vehicles, where annotated datasets are crucial for object detection, path planning, and safety assurance. Furthermore, the increasing complexity of visual data and the necessity for scalable annotation workflows are compelling organizations to invest in sophisticated annotation tools that offer automation, collaboration, and integration capabilities, thereby fueling market expansion.

Another significant growth driver is the rapid evolution of AI-powered applications in healthcare, retail, and security. In the healthcare sector, computer vision annotation tools are pivotal in training models for medical imaging diagnostics, disease detection, and patient monitoring. Similarly, in retail, these tools enable the development of intelligent systems for inventory management, customer behavior analysis, and automated checkout solutions. The security and surveillance segment is also witnessing heightened adoption, as annotated video data becomes essential for facial recognition, threat detection, and crowd monitoring. The convergence of these trends is accelerating the demand for advanced annotation platforms that can handle diverse data modalities and deliver high annotation accuracy at scale.

The increasing availability of cloud-based annotation solutions is further catalyzing market growth by offering flexibility, scalability, and cost-effectiveness. Cloud deployment models allow organizations to access powerful annotation tools remotely, collaborate with distributed teams, and leverage on-demand computing resources. This is particularly advantageous for large-scale projects that require the annotation of millions of images or videos. Moreover, the integration of automation features such as AI-assisted labeling, quality control, and workflow management is enhancing productivity and reducing time-to-market for AI solutions. As a result, both large enterprises and small-to-medium businesses are embracing cloud-based annotation platforms to streamline their AI development pipelines.

From a regional perspective, North America leads the Computer Vision Annotation Tool market, accounting for the largest revenue share in 2024. The region’s dominance is attributed to the presence of major technology companies, robust AI research ecosystems, and early adoption of computer vision solutions in sectors like automotive, healthcare, and security. Europe follows closely, driven by regulatory support for AI innovation and growing investments in smart manufacturing and healthcare technologies. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by expanding digital infrastructure, government initiatives to promote AI adoption, and the rise of technology startups. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a comparatively moderate pace, as organizations in these regions increasingly recognize the value of annotated data for digital transformation initiatives.

Component Analysis

The Computer Vision Annotation Tool market is segmented by component into software and services, each playing a distinct yet complementary role in the value chain. The software segment encompasses standalone annotation platforms, integrated development environments, and specialized tools designed for labeling images, videos, text, and audio. These solutions are characterized by fe

The global market for Image Tagging & Annotation Services is poised for significant expansion, projected to reach a market size of approximately $5,500 million in 2025. This growth is fueled by an impressive Compound Annual Growth Rate (CAGR) of 22% during the forecast period of 2025-2033. The burgeoning demand for AI and machine learning applications across various sectors is the primary catalyst, driving the need for meticulously tagged and annotated datasets to train these sophisticated models. Industries such as Automotive, particularly with the rise of autonomous driving and advanced driver-assistance systems (ADAS), are heavily investing in image annotation for object recognition and scene understanding. Similarly, Retail & Commerce leverages these services for personalized customer experiences, inventory management, and visual search functionalities. The Government & Security sector utilizes image annotation for surveillance, threat detection, and forensic analysis, while Healthcare benefits from its application in medical imaging analysis, diagnosis, and drug discovery. Further bolstering this growth are key trends like the increasing adoption of cloud-based annotation platforms, which offer scalability and enhanced collaboration, and the growing sophistication of annotation tools, including AI-assisted annotation that streamlines the process and improves accuracy. The demand for diverse annotation types, such as image classification, object recognition, and boundary recognition, is expanding as AI models become more complex and capable. While the market is robust, potential restraints include the high cost of skilled annotation labor and the need for stringent data privacy and security measures, especially in sensitive sectors like healthcare and government. However, the inherent value derived from accurate and comprehensive data annotation in driving AI innovation and operational efficiency across a multitude of industries ensures a dynamic and upward trajectory for this market. Here's a unique report description for Image Tagging & Annotation Services, incorporating your specific requirements:

This report offers an in-depth analysis of the global Image Tagging & Annotation Services market, a critical component for the advancement of Artificial Intelligence and Machine Learning. Valued at over $500 million in the base year of 2025, the market is projected to witness robust growth, reaching an estimated $2.5 billion by 2033. The study encompasses the historical period from 2019-2024, the base year of 2025, and a comprehensive forecast period spanning from 2025-2033, providing a dynamic outlook on market evolution.

Here are a few use cases for this project:

1. Historical Weapon Classification: This computer vision model can be utilized by historians, archeologists, and museum curators to classify and catalog historical weapons and artifacts, including swords, arrows, guns, and knives, enabling them to better understand and contextualize the weapons' origins and usage throughout history.

2. Video Game Asset Management: Game developers can use the Data Annotate model to automatically tag and categorize in-game assets, such as weapons and visual effects, to streamline their development process and more easily manage game content.

3. Prop and Costume Design: The model can aid prop and costume designers in the film, theater, and cosplay industries by identifying and categorizing various weapons and related items, allowing them to find suitable props or inspirations for their designs more quickly.

4. Law Enforcement and Security: Data Annotate can be used by law enforcement agencies and security personnel to effectively detect weapons in surveillance footage or images, enabling them to respond more quickly to potential threats and uphold public safety.

5. Educational Applications: Teachers and educators can use the model to develop interactive and engaging learning materials in the fields of history, art, and technology. It can help students identify and understand the significance of various weapons and their roles in shaping human history and culture.

The global Image Tagging and Annotation Services market is projected for robust expansion, estimated to reach approximately $2.5 billion in 2025. This growth trajectory is underpinned by a compound annual growth rate (CAGR) of around 18% anticipated from 2025 to 2033. This significant upward trend is primarily propelled by the escalating demand for high-quality labeled data across diverse industries, crucial for training and validating sophisticated Artificial Intelligence (AI) and Machine Learning (ML) models. Key applications driving this demand include the automotive sector for autonomous driving systems, the retail & eCommerce industry for product recognition and personalized experiences, and the BFSI sector for fraud detection and risk assessment. The burgeoning adoption of AI-powered solutions in healthcare for diagnostics and drug discovery, alongside the need for enhanced security and surveillance, further fuels the market's expansion. Furthermore, the increasing complexity of AI algorithms necessitates more precise and nuanced annotation types, such as semantic segmentation and advanced object recognition, thereby expanding the service offerings and market value. The market is characterized by several dynamic trends and some restraining factors. A significant trend is the rise of specialized annotation platforms and AI-assisted annotation tools, which enhance efficiency and accuracy while reducing turnaround times. Cloud-based annotation services are also gaining traction due to their scalability and accessibility. However, challenges persist, including the scarcity of skilled annotators capable of handling complex tasks and maintaining data privacy and security standards, which can act as restraints. Geographically, North America, led by the United States, currently holds a substantial market share, driven by early adoption of AI technologies and significant investment in R&D. Asia Pacific, particularly China and India, is emerging as a rapidly growing region, owing to a large pool of skilled labor and increasing investments in AI infrastructure. The competitive landscape features a blend of established global players and emerging niche providers, all vying to capture market share through technological innovation, service differentiation, and strategic partnerships. This comprehensive report delves into the dynamic landscape of Image Tagging and Annotation Services, analyzing market dynamics, key trends, and future projections. The study covers the historical period from 2019 to 2024, with a base year of 2025 and an estimated year also of 2025, projecting growth through 2033. The market is valued in the millions, reflecting its significant economic impact and growing importance across various industries.

WSI Annotation Services Market Outlook

As per our latest research, the global WSI Annotation Services market size stood at USD 1.42 billion in 2024, reflecting robust expansion driven by advancements in artificial intelligence and machine learning applications across diverse sectors. The market is expected to grow at a CAGR of 23.7% from 2025 to 2033, reaching a forecasted value of USD 11.19 billion by 2033. The primary growth factor fueling this remarkable trajectory is the surging demand for high-quality annotated data to train sophisticated AI models, particularly in sectors like autonomous vehicles, healthcare diagnostics, and retail automation.

One of the most significant growth drivers for the WSI Annotation Services market is the escalating adoption of AI-powered solutions across industries. As artificial intelligence becomes increasingly integral to business processes and consumer products, the necessity for accurately annotated data has soared. Companies are leveraging WSI annotation services to enhance the precision of machine learning algorithms, particularly in image, text, video, and audio data domains. This trend is particularly pronounced in sectors such as autonomous vehicles, where annotated data is essential for object detection and navigation, and in healthcare, where annotated medical images underpin diagnostic AI tools. The proliferation of digital transformation initiatives and the need to process large volumes of unstructured data further amplify the market’s expansion.

Another critical growth factor is the rapid evolution of data annotation technologies and methodologies. The market has witnessed substantial investments in automation tools, cloud-based platforms, and AI-assisted annotation frameworks that streamline the annotation process, enhance accuracy, and reduce turnaround times. These advancements are making WSI annotation services more accessible and cost-effective for organizations of all sizes, from startups to large enterprises. Furthermore, the growing emphasis on data privacy and regulatory compliance has spurred the adoption of secure, on-premises, and hybrid deployment models, broadening the market’s appeal across highly regulated industries such as BFSI and healthcare. The integration of advanced quality control mechanisms and scalable annotation workflows has further reinforced market growth.

The increasing focus on industry-specific applications is also propelling the WSI Annotation Services market forward. In retail and e-commerce, for instance, annotated data is pivotal for developing recommendation engines, visual search tools, and customer sentiment analysis. In agriculture, annotation services enable the deployment of precision farming technologies by facilitating crop and livestock monitoring through annotated images and sensor data. The security and surveillance sector is leveraging annotation for facial recognition, anomaly detection, and threat assessment. This diversification of use cases is driving demand for specialized annotation services tailored to the unique requirements of each industry, thereby expanding the market’s scope and value proposition.

From a regional perspective, North America continues to dominate the WSI Annotation Services market, accounting for the largest revenue share in 2024, closely followed by Europe and the Asia Pacific. The presence of leading technology companies, robust digital infrastructure, and a mature AI ecosystem are key factors underpinning North America’s leadership. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid digitization, increasing investments in AI research, and the proliferation of tech startups. Europe’s market growth is supported by strong regulatory frameworks and a focus on ethical AI development. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions increasingly adopt AI-driven solutions.

Type Analysis

The WSI Annotation Services market by

Synthetic Data for Video Surveillance Market Outlook

According to our latest research, the global synthetic data for video surveillance market size was valued at USD 560 million in 2024, with a robust compound annual growth rate (CAGR) of 38.7% expected from 2025 to 2033. This dynamic growth trajectory will propel the market to reach USD 7.85 billion by 2033. The primary driver behind this exceptional market expansion is the increasing demand for advanced video analytics in security and surveillance, coupled with the need for privacy-compliant data to train artificial intelligence (AI) and machine learning (ML) models effectively.

One of the most compelling growth factors for the synthetic data for video surveillance market is the growing adoption of AI-powered surveillance systems across critical sectors such as government, transportation, and retail. As organizations strive to enhance the accuracy and reliability of video analytics, the scarcity and privacy concerns associated with real-world surveillance data have become significant hurdles. Synthetic data addresses these challenges by generating highly realistic, annotated datasets that can be used to train and validate AI models without exposing sensitive information. This capability not only accelerates AI model development but also ensures compliance with stringent data privacy regulations such as GDPR and CCPA, making synthetic data an indispensable asset for next-generation surveillance solutions.

Another key growth driver is the rapid technological advancements in generative AI and computer vision, which have significantly improved the quality and diversity of synthetic video data. Modern synthetic data platforms can now simulate complex environments, diverse lighting conditions, and varied object interactions, providing a rich training ground for surveillance algorithms. This technological leap has enabled the deployment of robust video analytics for applications such as object detection, activity recognition, and anomaly detection, even in scenarios where real-world data collection is impractical or cost-prohibitive. As a result, organizations are increasingly leveraging synthetic data to bridge the gap between limited real-world datasets and the high-performance requirements of contemporary video surveillance systems.

The market is further propelled by the escalating need for scalable, cost-effective data solutions in the face of rising security threats and expanding surveillance networks. Traditional data collection and annotation processes are time-consuming, expensive, and often restricted by privacy laws. Synthetic data, on the other hand, offers a scalable alternative that can generate vast amounts of labeled data on demand, significantly reducing development timelines and operational costs. This scalability is particularly crucial as smart cities, transportation hubs, and large enterprises expand their surveillance infrastructure to monitor larger areas and more complex environments. By enabling rapid prototyping and deployment of AI-driven surveillance applications, synthetic data is becoming a cornerstone of digital transformation in the security sector.

From a regional perspective, North America currently leads the synthetic data for video surveillance market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the early adoption of AI technologies, a strong presence of leading technology vendors, and robust investments in public safety initiatives. Europe’s market growth is supported by stringent privacy regulations and increasing demand for privacy-preserving AI solutions, while Asia Pacific is emerging as a high-growth region due to rapid urbanization, smart city projects, and expanding surveillance networks. The collective momentum across these regions underscores the global shift towards data-driven, privacy-centric surveillance strategies powered by synthetic data.

Component Analysis

The synthetic data for video surveillance market is segmented by component into software and services. The software segment currently dominates the market, accounting for the majority of the global revenue share in 2024. This dominance is driven by the proliferation of advanced synthetic data generation tools and platforms that enable the creation of highly realistic video datasets tailored for surveillance applicati

Video Dataset Labeling for Security Market Outlook

According to our latest research, the global Video Dataset Labeling for Security market size reached USD 1.84 billion in 2024, with a robust year-over-year growth rate. The market is expected to expand at a CAGR of 18.7% from 2025 to 2033, ultimately achieving a projected value of USD 9.59 billion by 2033. This impressive growth is driven by the increasing integration of artificial intelligence and machine learning technologies in security systems, as well as the rising demand for accurate, real-time video analytics across diverse sectors.

One of the primary growth factors for the Video Dataset Labeling for Security market is the escalating need for advanced surveillance solutions in both public and private sectors. As urban environments become more complex and security threats more sophisticated, organizations are increasingly investing in intelligent video analytics that rely on meticulously labeled datasets. These annotated datasets enable AI models to accurately detect, classify, and respond to potential threats in real-time, significantly enhancing the effectiveness of surveillance systems. The proliferation of smart cities and the adoption of IoT-enabled devices have further amplified the volume of video data generated, necessitating efficient and scalable labeling solutions to ensure actionable insights and rapid incident response.

Another significant driver is the evolution of regulatory frameworks mandating higher standards of security and data privacy. Governments and industry bodies across the globe are implementing stringent guidelines for surveillance, especially in critical infrastructure sectors such as transportation, BFSI, and energy. These regulations not only require comprehensive monitoring but also demand that video analytics systems minimize false positives and ensure accurate identification of individuals and behaviors. Video dataset labeling plays a pivotal role in training AI models to comply with these regulations, reducing the risk of compliance breaches and supporting forensic investigations. The need for transparency and accountability in automated security solutions is further pushing organizations to invest in high-quality labeling services and software.

Technological advancements in deep learning and computer vision have also catalyzed market growth. The development of sophisticated annotation tools, automation platforms, and cloud-based labeling services has significantly reduced the time and cost associated with preparing training datasets. Innovations such as active learning, semi-supervised labeling, and synthetic data generation are making it possible to annotate vast volumes of video footage with minimal manual intervention, thereby accelerating AI model deployment. Furthermore, the integration of multimodal data—combining video with audio, thermal, and biometric inputs—has expanded the scope of security applications, driving demand for more comprehensive and nuanced labeling solutions.

From a regional perspective, North America currently leads the global Video Dataset Labeling for Security market, accounting for approximately 37% of the total market share in 2024. This dominance is attributed to the region's early adoption of AI-driven security solutions, substantial investments in smart infrastructure, and the presence of leading technology providers. Europe and Asia Pacific are also witnessing rapid growth, fueled by government initiatives to modernize public safety systems and the increasing incidence of security threats in urban and industrial environments. The Asia Pacific region, in particular, is expected to register the highest CAGR over the forecast period, driven by large-scale deployments in countries such as China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by growing urbanization and heightened security concerns.

Component Analysis

The Video Dataset Labeling for Secu

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Artificial Intelligence (AI) Camera Market Size 2024-2028

The artificial intelligence (ai) camera market size is valued to increase USD 8.43 billion, at a CAGR of 13.64% from 2023 to 2028. Need for surveillance and security across smart cities will drive the artificial intelligence (ai) camera market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 44% growth during the forecast period.
By Application - Entrance/exit surveillance camera for store/supermarkets segment was valued at USD 564.90 billion in 2022
By Type - Smartphone cameras segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 162.41 million
Market Future Opportunities: USD 8434.60 million
CAGR : 13.64%
APAC: Largest market in 2022

Market Summary

The market is a dynamic and rapidly expanding sector, driven by the integration of advanced technologies and increasing demand for intelligent surveillance and security solutions. Core technologies, such as deep learning algorithms and computer vision, are revolutionizing the way AI cameras process and analyze visual data, enabling more accurate and efficient identification of objects and activities. Applications span across various industries, including transportation, retail, and healthcare, with smart cities being a significant growth area. According to recent reports, the global AI camera market is expected to reach a significant market share in the next few years, driven by the need for enhanced security and surveillance.
Strategic alliances between market players and technology providers are also fueling innovation and growth. However, concerns around data security and privacy remain major challenges, necessitating robust regulatory frameworks and secure data handling practices.

What will be the Size of the Artificial Intelligence (AI) Camera Market during the forecast period?

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How is the Artificial Intelligence (AI) Camera Market Segmented and what are the key trends of market segmentation?

The artificial intelligence (ai) camera industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Entrance/exit surveillance camera for store/supermarkets
  Anomaly detection camera for factories and work site
  Camera for elevators
  Others


Type

  Smartphone cameras
  Surveillance cameras
  Others


Geography

  North America

    US
    Canada


  Europe

    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By Application Insights

The entrance/exit surveillance camera for store/supermarkets segment is estimated to witness significant growth during the forecast period.

In the realm of building security and detection solutions, AI cameras play a pivotal role in safeguarding people and property in establishments such as supermarkets. The market for these advanced security devices is experiencing significant growth due to escalating demands for surveillance systems to prevent unauthorized access and secure workplaces. The increasing concerns over global security, fueled by a rise in terror activities, have led to substantial investments in camera systems worldwide. This burgeoning trend is anticipated to boost the adoption of AI cameras in supermarkets, thereby propelling the expansion of the global market. Moreover, the integration of advanced technologies like video compression codecs, neural network architecture, deep learning models, and object tracking precision in AI cameras is revolutionizing the industry.

These enhancements enable more efficient and accurate real-time video processing, image classification, and object detection. Furthermore, the integration of machine learning pipelines, privacy-preserving AI, and intrusion detection systems is bolstering the capabilities of AI cameras. Additionally, the market is witnessing the emergence of cloud-based AI cameras, which offer the advantages of multi-camera calibration, data annotation tools, and risk assessment algorithms. These advancements facilitate the seamless integration of AI-powered surveillance systems with other security technologies, such as low-light image enhancement, facial recognition technology, and license plate recognition. Anomaly detection systems, sensor fusion techniques, and edge computing deployment are some of the other innovative technologies that are gaining traction in the market.

These advancements are enabling AI cameras to deliver more accurate and reliable security solutions, while also ensuring IoT device connectivity and traffic monitoring systems. Furthermore, the market is expected to witness a substantial increase in demand for high-resolution

This is an open-source synthetic dataset for computer vision object detection, focused on people holding knives in public and semi-public environments, viewed from CCTV and surveillance camera perspectives. It is designed to help train and evaluate YOLO, YOLOv8, YOLOWorld, Detectron, and other object detection models for threat recognition, security analytics, and abnormal behavior detection.

Key Features

Classes: person, knife Annotations: YOLO format (bounding boxes, normalized) Image Type: Synthetic, realistic, CCTV-style angles Scenes: Indoor/outdoor, airports, walkways, corridors, public spaces Purpose: Threat detection, surveillance AI, safety analytics, security CV research Size: 114 high-quality annotated images (sample version)

This is a sample dataset created by Simuletic. Larger knife detection sets (3K+ images) and custom scene generation (security, airport, military, intruder, behavior) are available at https://simuletic.com

images/ → .jpg or .png image files
labels/ → YOLO annotation .txt files (same file name as images)
annotations.csv → (optional) structured label overview

class_id center_x center_y width height 0 0.45 0.55 0.20 0.30 # person 1 0.63 0.60 0.15 0.18 # knife

path: /path/to/data train: images val: images names: 0: person 1: knife

Potential use cases:

Knife detection: Identify knives in CCTV/security environments Threat detection: Detect armed individuals in public spaces Surveillance training: Train security camera anomaly models Synthetic data research: Test synthetic-to-real domain transfer

Ethics & Considerations Fully synthetic — no real individuals or incidents depicted Created to support security, safety, and ethical AI research and implementation May not represent full real-world diversity — see our larger dataset for full diversity.

License Creative Commons Attribution 4.0 (CC BY 4.0) You may share, modify, and use commercially, as long as credit to Simuletic is given.

Citation @dataset{simuletic_knife_detection_2025, author = {Simuletic}, title = {Simuletic Synthetic Knife Detection CCTV Dataset}, year = {2025}, url = {https://simuletic.com} }

Related Links Website: https://simuletic.com Weapon Detection Dataset (previous release) https://www.kaggle.com/datasets/simuletic/cctv-weapon-dataset Github & Hugging Face links coming soon

Questions or custom dataset requests? Visit https://simuletic.com or message via Kaggle / Hugging Face.

Imaging Annotation Tools Market Outlook

According to our latest research, the global Imaging Annotation Tools market size reached USD 1.42 billion in 2024, reflecting robust demand across a range of industries. The market is projected to grow at a CAGR of 27.8% from 2025 to 2033, reaching an estimated USD 13.25 billion by 2033. This rapid expansion is driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies, which require high-quality annotated datasets to train models effectively. The escalating need for precise data labeling in applications such as medical imaging, autonomous vehicles, and security surveillance is further fueling growth in the imaging annotation tools market.

One of the primary growth factors for the imaging annotation tools market is the accelerating integration of AI and ML across various sectors. As organizations strive to automate processes and enhance decision-making, the demand for annotated image data has surged. In particular, sectors such as healthcare and automotive are leveraging these tools to improve diagnostic accuracy and enable advanced driver-assistance systems (ADAS), respectively. The proliferation of smart devices and the exponential growth in visual data generation also necessitate sophisticated annotation solutions, ensuring that AI models are trained with high-quality, accurately labeled datasets. The increasing complexity of AI applications is thus directly contributing to the expansion of the imaging annotation tools market.

Another significant driver is the evolution of deep learning algorithms, which rely heavily on large volumes of labeled data for supervised learning. The emergence of semi-automatic and automatic annotation tools is addressing the challenges posed by manual labeling, which can be time-consuming and prone to human error. These advanced tools not only accelerate the annotation process but also enhance accuracy and consistency, making them indispensable for industries with stringent quality requirements such as medical imaging and security surveillance. Furthermore, the growing adoption of cloud-based solutions has democratized access to powerful annotation platforms, enabling organizations of all sizes to participate in the AI revolution. This democratization is expected to further stimulate market growth over the forecast period.

The expanding use cases for imaging annotation tools across non-traditional sectors such as agriculture, retail, and robotics are also contributing to market momentum. In agriculture, annotated images are used to train AI models for crop monitoring, disease detection, and yield prediction. Retailers are harnessing these tools to enhance customer experience through visual search and automated inventory management. The robotics sector benefits from annotated datasets for object recognition and navigation, critical for the development of autonomous systems. As these diverse applications continue to proliferate, the imaging annotation tools market is poised for sustained growth, supported by ongoing innovation and increasing investment in AI technologies.

Automated Image Annotation for Microscopy is revolutionizing the way researchers and scientists handle vast amounts of visual data in the field of life sciences. By leveraging advanced AI algorithms, these tools are capable of accurately labeling complex microscopic images, which are crucial for tasks such as cell counting, structure identification, and anomaly detection. This automation not only speeds up the annotation process but also minimizes human error, ensuring that datasets are both comprehensive and precise. As microscopy generates increasingly large datasets, the demand for automated annotation solutions is growing, enabling researchers to focus more on analysis and discovery rather than manual data preparation. This technological advancement is particularly beneficial in medical research and diagnostics, where timely and accurate data interpretation can lead to significant breakthroughs.

From a regional perspective, North America currently dominates the imaging annotation tools market, driven by the presence of leading AI technology providers and a robust ecosystem for innovation. However, Asia Pacific is emerging as the fastest-growing region, fueled by rising investments in AI infrastructure, government in

Introduction

There have been many terrorist attacks and lone-wolf attacks around the world. These attacks have caused immense loss of life and resources. If we can detect weapons in civilian and commercial areas through intelligent surveillance, many of these attacks can be prevented.

Dataset collection

This dataset is a collection of images from 9 different types of weapons. Previously, there have been datasets that has only one class Weapon or Gun. This dataset consists of 9 classes as of now: Automatic Rifle, Bazooka, Handgun, Knife, Grenade Launcher, Shotgun, SMG, Sniper, Sword. This dataset was created with the help of simple_image_download library in Python, which downloads images from internet. 100 images from each class were collected. After inspection invalid images were discarded, leaving us with a total of 714 images for all 9 classes. https://i.pinimg.com/originals/9b/de/f2/9bdef269d54dc025c248848282d823e3.jpg" alt="Weapons">

Quick Summary

• Number of classes: 9
• Label Annotation: YOLO format (.txt)
• Metadata: metadata.csv provides information about the dataset and train-val split
• Weapon Class Map: {'Automatic Rifle': 0, 'Bazooka': 1, 'Grenade Launcher': 2, 'Handgun': 3, 'Knife': 4, 'Shotgun': 5, 'SMG': 6, 'Sniper': 7, 'Sword': 8}
• Difficulty: This is a beginner-friendly dataset on multi-class classification. The splits are given in the dataset folder itself with metadata, so anyone can use this data to run models and produce results.

References

1. Weapon Detection Dataset: https://dasci.es/transferencia/open-data/24705/
2. Weapon Detection for Security and Video Surveillance: https://sci2s.ugr.es/weapons-detection

Data Labeling Market Outlook

According to our latest research, the global data labeling market size reached USD 3.2 billion in 2024, driven by the explosive growth in artificial intelligence and machine learning applications across industries. The market is poised to expand at a CAGR of 22.8% from 2025 to 2033, and is forecasted to reach USD 25.3 billion by 2033. This robust growth is primarily fueled by the increasing demand for high-quality annotated data to train advanced AI models, the proliferation of automation in business processes, and the rising adoption of data-driven decision-making frameworks in both the public and private sectors.

One of the principal growth drivers for the data labeling market is the accelerating integration of AI and machine learning technologies across various industries, including healthcare, automotive, retail, and BFSI. As organizations strive to leverage AI for enhanced customer experiences, predictive analytics, and operational efficiency, the need for accurately labeled datasets has become paramount. Data labeling ensures that AI algorithms can learn from well-annotated examples, thereby improving model accuracy and reliability. The surge in demand for computer vision applications—such as facial recognition, autonomous vehicles, and medical imaging—has particularly heightened the need for image and video data labeling, further propelling market growth.

Another significant factor contributing to the expansion of the data labeling market is the rapid digitization of business processes and the exponential growth in unstructured data. Enterprises are increasingly investing in data annotation tools and platforms to extract actionable insights from large volumes of text, audio, and video data. The proliferation of Internet of Things (IoT) devices and the widespread adoption of cloud computing have further amplified data generation, necessitating scalable and efficient data labeling solutions. Additionally, the rise of semi-automated and automated labeling technologies, powered by AI-assisted tools, is reducing manual effort and accelerating the annotation process, thereby enabling organizations to meet the growing demand for labeled data at scale.

The evolving regulatory landscape and the emphasis on data privacy and security are also playing a crucial role in shaping the data labeling market. As governments worldwide introduce stringent data protection regulations, organizations are turning to specialized data labeling service providers that adhere to compliance standards. This trend is particularly pronounced in sectors such as healthcare and BFSI, where the accuracy and confidentiality of labeled data are critical. Furthermore, the increasing outsourcing of data labeling tasks to specialized vendors in emerging economies is enabling organizations to access skilled labor at lower costs, further fueling market expansion.

From a regional perspective, North America currently dominates the data labeling market, followed by Europe and the Asia Pacific. The presence of major technology companies, robust investments in AI research, and the early adoption of advanced analytics solutions have positioned North America as the market leader. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by the rapid digital transformation in countries like China, India, and Japan. The growing focus on AI innovation, government initiatives to promote digitalization, and the availability of a large pool of skilled annotators are key factors contributing to the regionÂ’s impressive growth trajectory.

In the realm of security, Video Dataset Labeling for Security has emerged as a critical application area within the data labeling market. As surveillance systems become more sophisticated, the need for accurately labeled video data is paramount to ensure the effectiveness of security measures. Video dataset labeling involves annotating video frames to identify and track objects, behaviors, and anomalies, which are essential for developing intelligent security systems capable of real-time threat detection and response. This process not only enhances the accuracy of security algorithms but also aids in the training of AI models that can predict and prevent potential security breaches. The growing emphasis on public safety and

Extracted Frames from UCA-Crime Dataset: Temporal Anomaly Detection in Surveillance Videos

Dataset Overview

This dataset comprises frames extracted from the UCA-Crime dataset, a precisely annotated version of the widely used UCF-Crime dataset, designed for research in video anomaly detection and event localization. Each frame in this collection represents crucial temporal segments where events occur, providing high-quality, event-focused visual data suitable for machine learning and computer vision tasks. This dataset supports research from the paper, "Yaqez: Enhancing Surveillance Security Real-time Suspicious Human Activity Detection", conducted by Ahd Abdulrahman Alsobhi, Amjaad Hasan Alsukhayri, Rahaf Arafah Ahmed, Sadeem Abdullah Alsawat, and Tiaf Meshal Aljuaid.

Key Features

• Total Frames:
• Resolution: Frames retain original resolution to preserve quality for detailed analysis.
• Format: Each frame is stored as a .png file, labeled as video_name_frame_number.png, making it easy to associate frames with specific videos and timestamps.
• Event-Specific Segmentation: Frames are extracted based on annotated intervals in JSON format, allowing targeted training on temporal segments where key events occur.
• Downsampling: To optimize storage and computation, every third frame from 30 fps videos has been selected, creating a 10 fps dataset that balances temporal information with data efficiency.

Annotations

The extracted frames are tied to JSON annotations from the UCA-Crime dataset, providing: - Video Names: Each video has a unique identifier. - Event Timestamps: Precise start and end times (0.1-second precision) for each annotated event. - Event Descriptions: Short textual descriptions for each event, available in the original JSON files, which describe actions, objects, or anomalies in the scene.

Dataset Structure

• Frames Directory: Contains individual .png frames organized by video name and frame number.
• Annotation File: Accompanying JSON files provide timestamped event descriptions, allowing researchers to filter, sort, and analyze frames corresponding to specific events.

Intended Use

This dataset is designed for researchers and practitioners in: - Video Anomaly Detection: Frame-by-frame analysis of anomalies in surveillance footage. - Temporal Action Localization: Pinpointing the start and end times of specific actions within video sequences. - Human Activity Recognition: Training models to recognize behaviors or activities in surveillance settings. - Scene Understanding: Leveraging frame annotations to support high-level video content comprehension.

Licensing and Usage

This dataset is intended for non-commercial, academic research in alignment with the original UCA-Crime and UCF-Crime datasets’ licensing terms. Proper citation and attribution to the creators of the UCA-Crime and UCF-Crime datasets are required.

Citations

If you use this dataset, please cite the original datasets:

• UCA-Crime:
  bibtex @misc{yuan2023surveillance, title={Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges}, author={Tongtong Yuan and Xuange Zhang and Kun Liu and Bo Liu and Chen Chen and Jian Jin and Zhenzhen Jiao}, year={2023}, eprint={2309.13925}, archivePrefix={arXiv}, primaryClass={cs.CV} }

• UCF-Crime:
  bibtex @inproceedings{sultani2018real, title={Real-world anomaly detection in surveillance videos}, author={Sultani, Waqas and Chen, Chen and Shah, Mubarak}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={6479--6488}, year={2018} }

This dataset is designed for training YOLO-based object detection models to identify humans and suspicious behavior in surveillance videos. The data consists of annotated frames extracted from various scenarios that mimic real-world theft and suspicious activity.

Context: This project aims to improve the accuracy and robustness of theft detection in real-time video feeds. It is particularly useful for security applications in shops, ATMs, and public areas.

Data Structure:

Images from 12 surveillance-style videos

Annotations in YOLO format

Two classes: human and suspicion

Inspiration: Inspired by real-world security challenges, the dataset is tailored for training models that can act as a first-alert system in automated surveillance setups.