Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tensorturtle/classy-sort-yolov5
Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
https://github.com/tensorturtle/classy-sort-yolov5
mot multi-object-tracking tracker yolo yolov5
Last synced: 12 days ago
JSON representation
Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
- Host: GitHub
- URL: https://github.com/tensorturtle/classy-sort-yolov5
- Owner: tensorturtle
- License: gpl-3.0
- Created: 2020-12-31T06:24:44.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-11-22T14:16:04.000Z (over 1 year ago)
- Last Synced: 2024-02-29T09:34:29.898Z (4 months ago)
- Topics: mot, multi-object-tracking, tracker, yolo, yolov5
- Language: Python
- Homepage:
- Size: 198 MB
- Stars: 107
- Watchers: 3
- Forks: 28
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-yolo-object-detection - tensorturtle/classy-sort-yolov5 - sort-yolov5?style=social"/> : Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation. (Applications)
README
# ℂ𝕝𝕒𝕤𝕤𝕪𝕊𝕆ℝ𝕋
by [Jason Sohn (website: jasonsohn.com)](https://jasonsohn.com)
ClassySORT is a simple real-time multi-object tracker (MOT) that works for any kind of object class (not just people).
## Introduction
ClassySORT is designed to be a state-of-the-art (SOTA) multi-object tracker (MOT) for use on your own projects. And bcause the You-only-look-once algorithm (YOLO) detector is pretrained on COCO dataset, ClassySORT can detect and count and track 80 different kinds of common objects 'out of the box'.
Tested on Pop_OS! 20.04 (similar to Ubuntu) and NVIDIA RTX 2070s.
Modifying it is exactly the same process as training YOLOv5 with your own dataset. [How do I do that?](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)
**ClassySORT implements**
+ [ultralytics/YOLOv5](https://github.com/ultralytics/yolov5/wiki) with no modifications
+ [abewley/SORT](https://github.com/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
Clone this repository
```bash
git clone https://github.com/tensorturtle/classy-sort-yolov5.git
cd classy-sort-yolov5
```
### Install Requirements
Python 3.8 or later with all requirements.txt. To install run:
```bash
pip install -r requirements.txt
```
### Download YOLOv5 weights
```bash
./download_weights.sh
```
This script will save yolov5 weights to `yolov5/weights/` directory.
### Run Tracking
To run the tracker on your own video and view the tracked bounding boxes, run:
```bash
python classy_track.py --source /path/to/video.mp4 --view-img
```
To get a summary of arguments run:
```bash
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:
```bash
[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](https://github.com/mikel-brostrom/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](https://github.com/GeekAlexis/FastMOT)
## License
ClassySORT is released under the GPL License version 3 to promote the open use of the tracker and future improvements.
Among other things, this means that code from this repository cannot be used for closed source distributions,
and you must license any derived code as GPL3 as well.