Video 2

## Overview

Video 2 is a dataset for object detection tasks - it contains Ball annotations for 430 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Project Overview:

The original goal was to use this model to monitor my rowing workouts and learn more about computer vision. To monitor the workouts, I needed the ability to identify the individual digits on the rowing machine. With the help of Roboflow's computer vision tools, such as assisted labeling, I was able to more quickly prepare, test, deploy and improve my YOLOv5 model. https://i.imgur.com/X1kHoEm.png" alt="Example Annotated Image from the Dataset">

https://i.imgur.com/uKRnFZc.png" alt="Inference on a Test Image using the rfWidget"> * How to Use the rfWidget

Roboflow's Upload API, which is suitable for uploading images, video, and annotations, worked great with a custom app I developed to modify the predictions from the deployed model, and export them in a format that could be uploaded to my workspace on Roboflow. * Uploading Annotations with the Upload API * Uploading Annotations with Roboflow's Python Package

What took me weeks to develop can now be done with the help of a single click utilize Roboflow Train, and the Upload API for Active Learning (dataset and model improvement). https://i.imgur.com/dsMo5VM.png" alt="Training Results - Roboflow FAST Model">

Dataset Classes:

• 1, 2, 3, 4, 5, 6, 7, 8, 9, 90 (class "90" is a stand-in for the digit, zero)

This dataset consits of 841 images. There are images from a different rowing machine and also from this repo. Some scenes are illuminated with sunlight. Others have been cropped to include only the LCD. Digits like 7, 8, and 9 are underrepresented.

For more information:

• A full tutorial for creating and deploying this model with YOLOv5: https://vision.philbrockman.com/

5 Video

## Overview

5 Video is a dataset for object detection tasks - it contains Objects annotations for 380 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Video Experiments

## Overview

Video Experiments is a dataset for object detection tasks - it contains Ball Player Referee annotations for 1,941 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Video Detection

## Overview

Video Detection is a dataset for object detection tasks - it contains Automatic Rifle Bazooka Grenad annotations for 776 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Violence Prediction In Surveillance Videos

## Overview

Violence Prediction In Surveillance Videos is a dataset for object detection tasks - it contains 'pistol', 'gun', 'knife', 'handgun', 'snipper', 'person', 'bazooka' annotations for 6,230 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

CS173 Video 1

## Overview

CS173 Video 1 is a dataset for object detection tasks - it contains Helmet annotations for 687 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Accident-Detection-Model

Accident Detection Model is made using YOLOv8, Google Collab, Python, Roboflow, Deep Learning, OpenCV, Machine Learning, Artificial Intelligence. It can detect an accident on any accident by live camera, image or video provided. This model is trained on a dataset of 3200+ images, These images were annotated on roboflow.

Problem Statement

• Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
• According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
• The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

Accidents survey

https://user-images.githubusercontent.com/78155393/233774342-287492bb-26c1-4acf-bc2c-9462e97a03ca.png" alt="Survey">

Literature Survey

• Sreyan Ghosh in Mar-2019, The goal is to develop a system using deep learning convolutional neural network that has been trained to identify video frames as accident or non-accident.
• Deeksha Gour Sep-2019, uses computer vision technology, neural networks, deep learning, and various approaches and algorithms to detect objects.

Research Gap

• Lack of real-world data - We trained model for more then 3200 images.
• Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
• Outdated Versions of previous works - We aer using Latest version of Yolo v8.

Proposed methodology

• We are using Yolov8 to train our custom dataset which has been 3200+ images, collected from different platforms.
• This model after training with 25 iterations and is ready to detect an accident with a significant probability.

Model Set-up

Preparing Custom dataset

• We have collected 1200+ images from different sources like YouTube, Google images, Kaggle.com etc.
• Then we annotated all of them individually on a tool called roboflow.
• During Annotation we marked the images with no accident as NULL and we drew a box on the site of accident on the images having an accident
• Then we divided the data set into train, val, test in the ratio of 8:1:1
• At the final step we downloaded the dataset in yolov8 format.
  #### Using Google Collab
• We are using google colaboratory to code this model because google collab uses gpu which is faster than local environments.
• You can use Jupyter notebooks, which let you blend code, text, and visualisations in a single document, to write and run Python code using Google Colab.
• Users can run individual code cells in Jupyter Notebooks and quickly view the results, which is helpful for experimenting and debugging. Additionally, they enable the development of visualisations that make use of well-known frameworks like Matplotlib, Seaborn, and Plotly.
• In Google collab, First of all we Changed runtime from TPU to GPU.
• We cross checked it by running command ‘!nvidia-smi’
  #### Coding
• First of all, We installed Yolov8 by the command ‘!pip install ultralytics==8.0.20’
• Further we checked about Yolov8 by the command ‘from ultralytics import YOLO from IPython.display import display, Image’
• Then we connected and mounted our google drive account by the code ‘from google.colab import drive drive.mount('/content/drive')’
• Then we ran our main command to run the training process ‘%cd /content/drive/MyDrive/Accident Detection model !yolo task=detect mode=train model=yolov8s.pt data= data.yaml epochs=1 imgsz=640 plots=True’
• After the training we ran command to test and validate our model ‘!yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=data.yaml’ ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt conf=0.25 source=data/test/images’
• Further to get result from any video or image we ran this command ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source="/content/drive/MyDrive/Accident-Detection-model/data/testing1.jpg/mp4"’
• The results are stored in the runs/detect/predict folder.
  Hence our model is trained, validated and tested to be able to detect accidents on any video or image.

Challenges I ran into

I majorly ran into 3 problems while making this model

• I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
• I was facing problem on cvat website because i was not sure what

Traffic Sign Detection Video Data Set 2

## Overview

Traffic Sign Detection Video Data Set 2 is a dataset for object detection tasks - it contains Traffic Signs annotations for 1,162 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

TE Data Annotation

## Overview

TE Data Annotation is a dataset for object detection tasks - it contains Objects annotations for 10,876 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Here are a few use cases for this project:

1. Educational Application: This model could be used in educational applications or games designed for children learning to recognize letters or digits. It could help in providing immediate feedback to learners by identifying whether the written letter or digit is correct.

2. Document Analysis: The model could be applied for document analysis and capturing data from written or printed material, including books, bills, notes, letters, and more. The numbers and special characters capability could be used for capturing amounts, expressions, or nuances in the text.

3. Accessibility Software: This model could be integrated into accessibility software applications aimed at assisting visually impaired individuals. It can analyze images or real-time video to read out the identified letters, figures, and special characters.

4. License Plate Recognition: Given its ability to recognize a wide array of symbols, the model could be useful for extracting information from license plates, aiding in security and law enforcement settings.

5. Handwritten Forms Processing: This computer vision model could be utilized to extract and categorize data from handwritten forms or applications, aiding in the automation of data entry tasks in various organizations.

Here are a few use cases for this project:

1. Autonomous Vehicles Navigation: This model could be vital in recognizing obstacles for self-driving two-wheeler vehicles. These vehicles could use the model to identify potential road hazards and navigate around them safely.

2. Road Maintenance and Safety: Local municipalities or city planning teams could use this model to proactively identify and repair road damage like potholes, subsidence, or loose surfaces, thus improving road safety.

3. Augmented Reality Touring: This model can be used in AR-based touring applications to provide cyclists or motorbike riders with real-time updates about upcoming obstacles or potential hazards on their route.

4. Game Development: Developers of driving or biking simulation games can use this model to make their virtual environments more realistic by including a diverse array of street obstacles that players must avoid.

5. Insurance claim processing: Insurance companies could utilize this system to assess the validity of claims related to two-wheeler accidents. The system could analyze video data to determine if reported obstacles were indeed present and had contributed to the accident.

Here are a few use cases for this project:

1. Golf Sports Broadcasting: This model could be used in live broadcasts or replay analysis to automatically identify and highlight key elements such as the player's swing technique, golf club position, and ball trajectory. This can enhance viewer experience by providing insightful, real-time details of the game.

2. Golf Training and Instruction: Golf instructors could use this model as a teaching tool to analyze and improve their students' golf playing techniques. They can better understand students' swing mechanics, club usage, and game-play strategies on the green.

3. Golf Game Software Development: Game developers can use it to create more realistic and interactive golf video games. The model can help generate a virtual environment that mirrors real-life scenarios, which can enhance the gaming experience for users.

4. Sports Equipment Marketing: Entities selling golf-related equipment or clothing can use the model to create interactive advertisements. It can detect the use of golfing objects in common videos and interlay their product ads or information.

5. Maintenance of Golf Courses: Golf course management could use this model to monitor the use and condition of their courses. By analyzing the visual data, they can identify the heavily used areas and potentially enhance the maintenance strategy accordingly.

Here are a few use cases for this project:

1. Public Safety Monitoring: This model could be used in surveillance systems to monitor public spaces, efficiently detecting weapons and alerting authorities of any potential danger.

2. Content Moderation: Social media companies or online platforms can utilize this model for moderating content, by automatically identifying and flagging content that features weaponry, which may violate their terms of service.

3. Airport Security: It could be applied for security checks in airports, public transportation stations or other security-sensitive areas, decreasing reliance on human personnel and potentially improving the efficiency and accuracy of detecting hidden or concealed weapons.

4. Military Use: This model can be used for drone or satellite imaging to detect weaponry, especially in conflict areas, which may provide vital intelligence information for defense forces.

5. Video Games & Entertainment: Game developers can use this model to identify weapon choices in multiplayer games or for creating realistic interactive responses in video games. Film productions can also use it to automate the process of categorizing and archiving weapon action scenes.

TE Data Annotation Trucks Only

## Overview

TE Data Annotation Trucks Only is a dataset for object detection tasks - it contains Objects SpdP annotations for 5,946 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Here are a few use cases for this project:

1. Video Game Design: Use the ApexYoloV6 model to identify and categorize various elements in video game screenshots. These can be user interfaces, in-game characters, items, or other relevant visual elements. This can aid in the development and refining of game graphics and interface design.

2. Video Game Analytics: Apply the model to detect and understand in-game events based on the screenshots. It can be used for creating heat maps of activity, understanding player behavior, or evaluating the game’s difficulty and balance.

3. Accessibility Tools: As the model can identify classes like 0, 2, 1, and x potentially representing objects or actions, it can be used to develop applications that make video games more accessible to individuals with certain disabilities. Recognizable classes can be transformed into accessible outputs like audio cues.

4. Live Streaming Enhancement: Use ApexYoloV6 to enhance live streaming platforms. Analyze the content of the game being streamed, provide real-time meta data for viewer interaction like game statistics or enhance the viewing experience by automating visual effects based on the recognized classes.

5. Game Tutorial Creation: The model can also be helpful in automated generation of video game guides or tutorials. By recognizing the particular game stages, actions or events from screenshots, it can help outline steps for passing a level or mastering a skill in the game.

Here are a few use cases for this project:

1. Game Development and Enhancement: Developers can incorporate the MKSC model into their game development process for identifying different game elements like characters or objects (coins, trees, peaches, etc.). This can facilitate automatic level design, character recognition and movement logic.

2. Interactive Content Creation: Streamers, digital content creators, or video game reviewers can use this model to analyze gameplay, identifying key characters and events in real-time or during video editing. This can open doors to more interactive and engaging content for audiences, possibly even automated highlights or recaps based on character occurrences.

3. Gaming Tutorials and Guides: The MKSC model can be used to develop comprehensive gaming guides and step-by-step tutorials. By recognizing game elements, it can show players where to find specific items or characters, or provide an analysis of gameplay to help players improve.

4. Machine Learning Research: Researchers can use the MKSC model as a baseline or reference for their research in video game AI or broader computer vision/ML studies. It provides a good use-case for pixel class recognition in complex, dynamic environments like video games.

5. Video Game AI Training: AI bots can be trained using the MKSC model. It can help build a neural network that understands video game landscapes, enabling the bots to interact more diversely and intelligently in a video game setup, and enhancing player vs. AI experiences.

Overview

This is a dataset of Chess board photos and various pieces. All photos were captured from a constant angle, a tripod to the left of the board. The bounding boxes of all pieces are annotated as follows: white-king, white-queen, white-bishop, white-knight, white-rook, white-pawn, black-king, black-queen, black-bishop, black-knight, black-rook, black-pawn. There are 2894 labels across 292 images.

https://i.imgur.com/nkjobw1.png" alt="Chess Example">

Follow this tutorial to see an example of training an object detection model using this dataset or jump straight to the Colab notebook.

Use Cases

At Roboflow, we built a chess piece object detection model using this dataset.

https://blog.roboflow.ai/content/images/2020/01/chess-detection-longer.gif" alt="ChessBoss">

You can see a video demo of that here. (We did struggle with pieces that were occluded, i.e. the state of the board at the very beginning of a game has many pieces obscured - let us know how your results fare!)

Using this Dataset

We're releasing the data free on a public license.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility.

Here are a few use cases for this project:

1. "Distracted Driving Prevention": This model can be used in safety applications to identify and alert drivers who are not focusing on the road. By identifying whether the driver's hand is on the wheel, phone, or holding a cup, it can help to monitor and prevent distracted driving.

2. "Consumer Behavior Analysis in Autonomous Cars": As self-driving vehicles grow in popularity, this model can be used to collect data on how users spend their time when not driving. Identifying how often they use their phone or drink from a cup could contribute to designing more useful and ergonomic interiors.

3. "Driver Monitoring System (DMS)": The model can be integrated into driver monitoring systems to ensure the driver's attention during manual or assisted driving. By recognizing if the driver's hand is on the wheel or elsewhere, it can contribute to safety measures like engaging an autopilot or issuing a warning sound if the driver is not holding the wheel.

4. "Thumbnail Generation for Driving Video Clips": The model can be used in media to automatically generate relevant thumbnails for video clips. By identifying key frames where the driver's hand is clearly visible on the wheel, a phone, or a cup, it can generate thumbnails that are informative about the content of the video.

5. "Data Collection for Ergonomic Studies": The model can contribute to ergonomic studies by collecting data on how drivers naturally position their hands while driving. This could provide valuable information for the design of more ergonomic car interiors or driving assist devices.

Here are a few use cases for this project:

1. Game Assistant: RustIRN_Mega can be used as an intelligent game assistant for Rust players, helping them quickly locate essential resources such as Stone nodes, Metal nodes, and Sulfur nodes, as well as players and bases, allowing new and experienced players to strategize and optimize their gameplay more efficiently.

2. Gaming Tutorials and Guides: RustIRN_Mega can be employed to generate visual aids and highlights for game tutorials and walkthroughs. The annotated images and videos can make it easier for content creators to explain the game, enhancing the learning experience for new players.

3. Game Monitoring and Cheating Detection: RustIRN_Mega can be used by game developers and server administrators to monitor and analyze in-game activities. The model can help identify suspicious behavior, such as exploiting game resources or cheating, enabling appropriate actions to be taken in order to maintain a fair gaming environment.

4. In-Game Video Editing and Highlight Reels: Content creators can utilize RustIRN_Mega to create engaging video content by automatically identifying key moments, such as significant resource discoveries or player interactions. The model enables quick and accurate video segmentation and annotation, saving time and effort in editing and creating high-quality videos for sharing with the gaming community.

5. Game Analytics and Player Behavior Studies: By analyzing large quantities of in-game images and video data, RustIRM_Mega can help researchers and developers study player behavior patterns, resource allocation strategies, and game dynamics. The insights gained can be used to improve game design, predict player actions, and optimize the overall gaming experience.