The accelerated proliferation of autonomous vehicles introduces significant challenges within the complex and dynamic landscapes of urban environments. This paper introduces the Intelligent Traffic Management System (ITMS), a groundbreaking solution designed to enhance the integration of self-driving vehicles and prioritize emergency response units in traffic management. We detail the design, development, and systematic evaluation of ITMS, which seeks to optimize intersection management by leveraging sophisticated computer vision techniques and intricate traffic logic algorithms. This system underscores a strategic approach towards mitigating traffic congestion, ensuring the seamless flow of vehicles, and significantly improving the efficacy of emergency responses in urban settings. Through ITMS, we aim to facilitate a more harmonious coexistence between autonomous and conventional vehicles, thereby contributing to the advancement of intelligent transportation systems.
- Yolov5 training on Custom Data (link to external repository)
- Deep Sort deep descriptor training (link to external repository)
- Yolov5 deep_sort pytorch evaluation
- Clone the repository recursively:
git clone --recurse-submodules https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch.git
If you already cloned and forgot to use --recurse-submodules
you can run git submodule update --init
- Make sure that you fulfill all the requirements: Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install, run:
pip install -r requirements.txt
Tracking can be run on most video formats
python3 track.py --source ... --show-vid # show live inference results as well
- Video:
--source file.mp4
- Webcam:
--source 0
- RTSP stream:
--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa
- HTTP stream:
--source http://wmccpinetop.axiscam.net/mjpg/video.mjpg
There is a clear trade-off between model inference speed and accuracy. In order to make it possible to fulfill your inference speed/accuracy needs you can select a Yolov5 family model for automatic download
python3 track.py --source 0 --yolo_weights yolov5s.pt --img 640 # smallest yolov5 family model
python3 track.py --source 0 --yolo_weights yolov5x6.pt --img 1280 # largest yolov5 family model
By default the tracker tracks all MS COCO classes.
If you only want to track persons I recommend you to get these weights for increased performance
python3 track.py --source 0 --yolo_weights yolov5/weights/crowdhuman_yolov5m.pt --classes 0 # tracks persons, only
If you want to track a subset of the MS COCO classes, add their corresponding index after the classes flag
python3 track.py --source 0 --yolo_weights yolov5s.pt --classes 16 17 # tracks cats and dogs, only
Here is a list of all the possible objects that a Yolov5 model trained on MS COCO can detect. Notice that the indexing for the classes in this repo starts at zero.
Can be saved to inference/output
by
python3 track.py --source ... --save-txt
If you find this project useful in your research, please consider cite:
@misc{yolov5deepsort2020,
title={Real-time multi-object tracker using YOLOv5 and deep sort},
author={Mikel Broström},
howpublished = {\url{https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch}},
year={2020}
}