Object-Tracking-PyTorch-YOLOv5-DeepSort

This Repository contains the implementation of Object Tracking using YOLOv5 and DeepSort in PyTorch. The combination of YOLOv5, a state-of-the-art Object Detection Model, and DeepSort, a robust deep learning-based tracking algorithm, enables accurate and efficient tracking of objects in video streams.

Requirements

• Python 3.6 or later
• PyTorch 1.7 or later
• torchvision
• NumPy
• OpenCV
• DeepSort

Configuration

You can adjust the configuration parameters in the config.yaml file to customize the tracking behavior, such as confidence thresholds, tracking IOU threshold, and more.

Citation

If you find this repository helpful, please consider citing the original YOLOv5 and DeepSort papers:

YOLOv5: YOLOv5 GitHub Repository:-https://github.com/ultralytics/yolov5

DeepSort PyTorch: DeepSORT PyTorch GitHub Repository:-https:-https://github.com/ZQPei/deep_sort_pytorch

Acknowledgments

This implementation builds upon the works of the YOLOv5 and DeepSort authors. Special thanks to the open-source community for their contributions.

Create and activate an environment

conda create -n objtracking python=3.7 -y

conda activate objtracking

For Further Details Follow the Commands.txt File

https://github.com/SuchindraKumar/Obj_Tracking_with_deepSort_-_yolov5/blob/main/Commands.txt