ℂ𝕝𝕒𝕤𝕤𝕪𝕊𝕆ℝ𝕋

ClassySORT is a simple real-time multi-object tracker (MOT) that works for any kind of object class (not just people). I created this to make it easier for people to create video object detection applications.

This is a one-person free time project. I will try to respond to Issues, but can't make any promises about support. Also, courtesy and motivating words like "please" and "thank you" go a long way.

I am curious to find out what you're using classy-sort-yolov5 for, so please tell me if you end up using this repository!

by Jason Sohn

Introduction

ClassySORT is designed to be a simple MOT for use on your own projects. And bcause the YOLO detector is pretrained on COCO, ClassySORT can detect and track 80 different kinds of common objects 'out of the box'. No supercomputer needed.

Modifying it is exactly the same process as training YOLO with your own dataset. How do I do that?

ClassySORT implements

ultralytics/YOLOv5 with no modifications
abewley/SORT with minor modifications

This repository uses a fixed version of YOLOv5 to ensure compatbility. Replacing the YOLOv5 code to the updated ultralytics/YOLOv5 code may result in breaking changes. If you are able to this without issues, please submit a pull request.

If you only need to track people, or have the resources to train a model from scratch with your own dataset, see 'More Complex MOTs' section below.

Using ClassySORT

Install Requirements

Python 3.8 or later with all requirements.txt. To install run:

pip install -r requirements.txt

Run Tracking

To run the tracker on your own video and view the tracked bounding boxes, run:

python classy_track.py --source /path/to/video.mp4 --view-img

To get a summary of arguments run:

python classy_track.py -h

The text results are saved to /inference/output/ from the array above in the following format. That location in the script is also a good point to plug your own programs into.

The saved text file contains the following information:

[frame_index, x_left_top, y_left_top, x_right_bottom, y_right_bottom, object_category, u_dot, v_dot, s_dot, object_id]

where

u_dot: time derivative of x_center in pixels
v_dot: time derivative of y_center in pixels
s_dot: time derivative of scale (area of bbox) in pixels

Implementation Details

Modifications to SORT

1. Class-aware Tracking

The original implementation of SORT threw away YOLO's object class information (0: person, 1: bike, etc.). I wanted to keep that information, so I added a detclass attribute to KalmanBoxTracker object in sort.py:

2. Kalman Filter parameters

I found that for my own dataset in which bounding boxes change size fairly quickly, the default Q value (process covariance) was too low. I recommend you try experimenting with them.

More Complex MOTs

If you only need to track people, or have the resources to train a model from scratch with your own dataset, then I recommend bostom/Yolov5_DeepSort_PyTorch. DeepSORT adds a separately trained neural network on top of SORT, which increases accuracy for human detections but slightly decreases performance. It also means that using your custom dataset involves training both YOLO and DeepSORT's 'deep association metric'

For a 'bag of tricks' optimized version of YOLOv5 + DeepSORT, see GeekAlexis/FastMOT

License

ClassySORT is released under the GPL License version 3 to promote the open use of the tracker and future improvements.

jinmc/classy-sort-yolov5