This repository contains a two-stage-tracker. The detections generated by YOLOv5 (https://github.com/ultralytics/yolov5) are passed to a Deep Sort algorithm (https://github.com/ZQPei/deep_sort_pytorch) which tracks the objects. It can track any object that your Yolov5 model was trained to detect.

• Yolov5 training on Custom Data (link to external repository) 
• Deep Sort deep descriptor training (link to external repository) 
• Yolov5 deep_sort pytorch evaluation 

1. 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

2. 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

3. Github block pushes of files larger than 100 MB. Hence you need to download two different weights: the ones for yolo and the ones for deep sort

• download the yolov5 weight from the latest realease, under assets. Place the downlaoded .pt file under yolov5/weights/
• download the deep sort weights. Place ckpt.t7 file underdeep_sort/deep/checkpoint/

Tracking can be run on most video formats

python3 track.py --source ...

• 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

By default the tracker tracks class 0, this is, persons. In this case I recommend you to get these weights for increased performance

parser.add_argument('--classes', nargs='+', default=[0], type=int, help='filter by class') # tracks persons only

If you want to track all 80 MS COCO classes just delete the default value

parser.add_argument('--classes', nargs='+', type=int, help='filter by class') # tracks all MS COCO classes

If you want to select only a few classes to track add their associated integer to default

parser.add_argument('--classes', nargs='+', default=[16, 17], type=int, help='filter by class') # 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

For more detailed information about the algorithms and their corresponding lisences used in this project access their official github implementations.