Surveillance camera statistics: which cities have the most CCTV cameras?

data source: https://www.comparitech.com/vpn-privacy/the-worlds-most-surveilled-cities/ cover image credit: https://www.pexels.com/photo/white-security-camera-3205735/

Living in the 20th century we know almost all of us have digital presence, which also includes surveillance. Surveillance need not always have to come from what we do online but also where we roam through surveillance cameras.

In this dataset I have consolidated the cities with the most surveillance cameras.

The data was extracted from: https://www.comparitech.com/vpn-privacy/the-worlds-most-surveilled-cities/

This is a point data set representing general locations of surveillance cameras within the City of Perth. Disclaimer: The City of Perth does not guarantee (either expressed or implied) the accuracy, completeness or timeliness of the information. Show full description

In Smart Cities, technologies are playing an important role in efficiently managing the rapid growth of the world's industrialization today. The deployment of surveillance cameras has proliferated to improve public safety and security. Many Closed-Circuit Television (CCTV) cameras have been installed to monitor and safeguard public spaces efficiently within the cities. Despite advancements in technology, video and image processing still largely rely on manual observation. This manual analysis is time-consuming, prone to missing critical details, and costly in terms of labor and resources. Nevertheless, monitoring large video feeds for long periods indicates fatigue, demise of focus, and errors, particularly when video surveillance is a necessity.
Road anomaly detection is one of the prominent computer vision issues that researchers have investigated to guarantee public safety. Road anomaly identification is increasingly difficult and complex due to the variety and complexity of abnormalities.
Deep learning algorithms must be efficient but also need a large dataset to train to recognize road anomalies in different environments. We proposed a custom real-world data set containing road anomaly images and videos that are made available to the public and private surveillance systems. Primary data were collected from diverse sites in Pakistan, and the data were gathered by recording videos and capturing images by using mobile and surveillance cameras The dataset encompasses five major categories of road anomaly effects.: vehicle accidents, vehicle fire, fighting, snatching(gunpoint), and potholes that classification modeling while promoting improvement in both scientific research and realistic application. The dataset also encompasses annotations with You Only Look Once (YOLO) based bounding boxes and class label files in text format for every image.
The researchers can utilize data to train and validate their anomaly detection algorithms and models, thus increasing public security and safety. This dataset focuses on natural environment scenes with a detailed examination of safe transportation and impacts on broader environmental knowledge. Data can give to the liable and ethical arrangement of Artificial Intelligence technologies in surveillance security system

This dataset reflects the daily volume of violations that have occurred in Children's Safety Zones for each camera. The data reflects violations that occurred from July 1, 2014 until present, minus the most recent 14 days. This data may change due to occasional time lags between the capturing of a potential violation and the processing and determination of a violation. The most recent 14 days are not shown due to revised data being submitted to the City of Chicago. The reported violations are those that have been collected by the camera and radar system and reviewed by two separate City contractors. In some instances, due to the inability the registered owner of the offending vehicle, the violation may not be issued as a citation. However, this dataset contains all violations regardless of whether a citation was issued, which provides an accurate view into the Automated Speed Enforcement Program violations taking place in Children's Safety Zones. More information on the Safety Zone Program can be found here: http://www.cityofchicago.org/city/en/depts/cdot/supp_info/children_s_safetyzoneporgramautomaticspeedenforcement.html. The corresponding dataset for red light camera violations is https://data.cityofchicago.org/id/spqx-js37.

This dataset shows the location, first operational date, and approaches of the speed cameras in the City of Chicago. The approach describes the originating direction of travel which is monitored by a speed camera. To attempt to make historical versions of this dataset more available, "Dataset Changelog" is enabled at the bottom of the main page. We cannot guarantee that the archival records will remain permanently so advise downloading any you think you may want. Some intermediate versions, especially minor changes, may not be visible. The map based on this dataset will not have past versions.

The Private Security Camera Voucher Program Program provides a private security camera system to eligible residents free of charge. District residents—either owners or tenants—who receive public assistance may be eligible to have a camera system installed at their home. Questions about the rebate or voucher program, please contact us at security.cameras@dc.gov or 202-727-5124. For more information, visit ovsjg.dc.gov.

The Private Security Camera Rebate Program creates a rebate for residents, businesses, nonprofits, and religious institutions to purchase and install security camera systems on their property and register them with the Metropolitan Police Department (MPD). The program provides a rebate of up to $200 per camera, with a maximum rebate of up to $500 per residential address (e.g., home offices, condo buildings, and apartments) and $750 for all other eligible addresses. The rebate is exclusively for the cost of the camera(s) including any applicable tax. Questions about the rebate or voucher program, please contact at security.cameras@dc.gov or 202-727-5124. For more information, visit https://ovsjg.dc.gov/service/private-security-camera-system-incentive-program.

This dataset reflects the daily volume of violations created by the City of Chicago Red Light Program for each camera. The data reflects violations that occurred from July 1, 2014 until present, minus the most recent 14 days. This data may change due to occasional time lags between the capturing of a potential violation and the processing and determination of a violation. The most recent 14 days are not shown due to revised data being submitted to the City of Chicago during this period. The reported violations are those that have been collected by the camera system and reviewed by two separate City contractors. In some instances, due to the inability the registered owner of the offending vehicle, the violation may not be issued as a citation. However, this dataset contains all violations regardless of whether a citation was actually issued, which provides an accurate view into the Red Light Program. Because of occasional time lags between the capturing of a potential violation and the processing and determination of a violation, as well as the occasional revision of the determination of a violation, this data may change. More information on the Red Light Program can be found here: http://www.cityofchicago.org/city/en/depts/cdot/supp_info/red-light_cameraenforcement.html. The corresponding dataset for speed camera violations is https://data.cityofchicago.org/id/hhkd-xvj4.

Location of 241 CCTV traffic poles within Dublin City Council administrative area. This dataset contains Road_1, Latitude and Longitude values.

The mayor can place cameras on the street if necessary for safety. These cameras are mainly located in the city centre. But they can also be placed in other places in the city if necessary for safety.

There are two types of cameras. Fixed cameras are installed for four years at a time. Then they are evaluated. These cameras are placed for a long time in areas with the most traffic and the most incidents.

The municipality also has flexible cameras that are installed for short periods from a few days to a maximum of six months.

Thanks to live camera surveillance, emerging incidents can be spotted quickly. Then the police or an enforcer can be sent to the incident. The images may also be used by the police for investigation purposes. Camera surveillance also has a preventive effect and many residents, visitors and entrepreneurs feel safer because of the cameras. Municipal observers monitor these cameras live almost 24 hours a day. The images are kept for 28 days and then deleted. Except if images are needed for investigation: they are kept longer by the police. All cameras are placed on the basis of designation decisions taken by the mayor. These decisions will be published on this page .

Read more about how the camera images are secured here.

Liverpool City Council has device counters deployed throughout the city centre to understand how the city is used. Data in this dashboard is derived from Meshed nCounters and CCTV retrofitted with an algorithm developed by University of Wollongong. The algorithm has used machine learning to identify objects in the camera viewfinder and categorise them into pedestrian, vehicle or bicycle. This transforms the CCTV into visual sensors. The nCounters generate data by counting the number of Wi-Fi signals emitted by non-identifiable mobile devices within a specified proximity and performing certain filtering and processing. No individuals are identified by either method. Both methods have advantages and limitations.Advantages of the nCounter method:Can provide insightful data on crowd sizes and individualsAn individual with one device will be counted onceThe nCounter can report the average ‘dwell time’ of the deviceLimitations of the nCounter method:individuals without devices will not be counted (for example young children or people without smart phones),if someone is carrying a smart phone which is in aeroplane mode or switched off then it will not be counted, andindividuals with multiple devices will be counted by the number of devices they have. For example, one person may have two smart phones, therefore the individual will be counted more than once.The visual sensors (CCTV) count the number of bicycles, people and vehicles in the location.Advantages of the visual sensor method:Can provide insightful data on pedestrian, vehicle and bicycle numbers,Re-uses existing common technology on city streets without further visual clutter,Does not rely on individuals carrying their own devices, so useful in areas with lower technology uptakeLimitations of visual sensor method:Individuals can be counted multiple times as they exit and re-enter the camera viewfinder,The machine learning cannot differentiate between bicycles and motorbikesData collected in this dataset can be visualised in the Liverpool City Centre activity dashboard

Certain areas of the City of Sydney have higher rates of crime than others. We have installed street safety (CCTV) cameras in these areas identified by the NSW Bureau of Crime Statistics and Research to help NSW Police detect, prevent and prosecute assaults and robberies, and other serious offences such as property damage.This dataset contains the location of street safety cameras in the City of Sydney.View this dataset in the street safety cameras app.For more information on street safety cameras, visit the City of Sydney website.

Citations issued from traffic safety cameras throughout New Orleans since April 2008.

For more information about this data, see: https://data.nola.gov/dataset/Traffic-Camera-Locations/htp7-qv47

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the City of Chicago. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore the City of Chicago using Kaggle and all of the data sources available through the City of Chicago organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under the following licenses: Public Domain

Traffic count data collected from the several GRIDSMART optical traffic detectors deployed by the City of Austin. This dataset is no longer updated because these devices are no longer maintained The Travel Detectors dataset ( https://data.austintexas.gov/Transportation-and-Mobility/Traffic-Detectors/qpuw-8eeb ) is related to this dataset using the 'ATD Device ID' field. The Travel Detectors dataset provides more information on device location and status. The average speed measurements may not have been calibrated for all intersections. All measurements have been collected using automated machine vision processes and have not been validated.

Certain areas of the City of Sydney have higher rates of crime than others. We have installed street safety (CCTV) cameras in these areas identified by the NSW Bureau of Crime Statistics and Research to help NSW Police detect, prevent and prosecute assaults and robberies, and other serious offences such as property damage. This app shows the location of street safety cameras in the City of Sydney.For more information on street safety cameras, visit the City of Sydney website.

Aggressive Behavior Video Classification, Object Detection dataset

WARNING: People in the videos exhibit aggressive behavior

The dataset with videos depicting people exhibiting aggressive and non-aggressive behavior is intended for classification purposes. It consists of a collection of video files that capture various individuals engaging in different activities and displaying distinct behavioral patterns and CSV-file with classification.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

Aggressive Behavior Video Classification Dataset can have multiple applications, such as surveillance systems, security modules, or social behavior analysis platforms.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4c8444fb8ddba04b0b0191d3517af3c6%2Ffreecompress-ezgif.gif?generation=1697023398942461&alt=media" alt="">

Dataset structure

The dataset consists of: - files: folder with videos with people exhibiting aggressive and non-aggressive behaviour (subfolders "aggressive" and "non_aggressive" respectively), - .csv file: path of each video in the "files" folder and classification of the behavoir

People Behavior Video Classification might be made in accordance with your requirements.

🧩 This is just an example of the data. Leave a request here to learn more

🚀 You can learn more about our high-quality unique datasets here

keywords: violence detection, violence classification, violent activity, violent crimes, real life violence detection, biometric dataset, biometric data dataset, object detection, public safety, human video, deep learning dataset, human video dataset, video dataset, video classification, computer vision, machine learning, cctv, camera detection, surveillance, security camera, security camera object detection, video-based monitoring, smart city, smart city development, smart city vision, smart city deep learning, smart city management

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

TRIMARC (Traffic Response and Incident Management Assisting the River City) camera locations in Louisville Metro Kentucky. This feature layer was created from a TRIMARC JSON files of camera locations. This item includes description, direction, and videos links and is used in the Louisville Metro Snow Map. The cameras are used to monitor the roadways and verify incidents to assist in freeway and incident management This feature is a static extract and will be reviewed before each snow season for updates. For more information on this feature layer and it's use please contact Louisville Metro GIS or LOJIC. To learn more about TRIMARC please visit the following website http://www.trimarc.org.