hyoooo2 / traffic_camera_tracking

This project aims to recognize and track escooters through the traffic intersection and find their trajectories, time of passing and speed.

Traffic Camera Tracking (SAVeNoW)

Basic Overview

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Main goals of this project is to detect, track and obtain velocity information of three classes (Escooter, Pedestrians and Cyclists) from a video footage of a traffic intersection in Ingolstadt. This project is a part of SaveNoW, where all these data are used to create a digital twin of Ingolstadt for simulation purposes.

Individual Components

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Object Detection:

(Still actively developed..A lot of the parameters could change in future)

PyTorch based YoloV5 framework is used for detection and the model yolov5l6 is trained on custom dataset of around 87k images in varied conditions (daylight, rain and night). All the images were manually labelled using CVAT online annotation tool. The model was trained by for 100+ hours using RTX 3090 for 600 epochs and reaches around 97% accuracy.

For faster inference speeds, a TensorRT Engine has been built and can infer at 125 fps

Tracker:

Since YoloV5 is spatial CNN Object detector, it doesn't contain temporal information to recognize an object across multiple frames. For this purpose, a modified SORT (Simple Online Realtime Tracker) has been used.

Velocity Estimation:

Using predetermined Camera Calibration values, the homography is calculated and every point in image is mapped accurately to it's correspoinding 3D world coordinates.

Sample inference:

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Notes

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• Weights files and training datasets are not present in the repo. If you are interested, please feel free to contact us: [email protected]
• Calibration values must be properly tuned depending on the individual scene (Hint: Maybe will be automated in the future)

Contributing

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Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.