## Introduction
This repository contains a highly configurable two-stage-tracker that adjusts to different deployment scenarios. The detections generated by [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv7](https://github.com/WongKinYiu/yolov7), [YOLOv8](https://github.com/ultralytics/ultralytics) a family of object detection architectures and models pretrained on the COCO dataset, are passed to [StrongSORT](https://github.com/dyhBUPT/StrongSORT)[](https://arxiv.org/pdf/2202.13514.pdf) which combines motion and appearance information based on [OSNet](https://github.com/KaiyangZhou/deep-person-reid)[](https://arxiv.org/abs/1905.00953) in order to tracks the objects. It can track any object that your Yolov5 model was trained to detect.
## Before you run the tracker
1. Clone the repository recursively:
`git clone --recurse-submodules https://github.com/bharath5673/StrongSORT-YOLO.git `
If you already cloned and forgot to use `--recurse-submodules` you can run `git submodule update --init`
2. Make sure that you fulfill all the requirements: Python 3.8 or later with all [requirements.txt](https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch/blob/master/requirements.txt) dependencies installed, including torch>=1.7. To install, run:
`pip install -r requirements.txt`
## Tracking sources
Tracking can be run on most video formats
## Select object detectors and ReID model
### Yolov5
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
```bash
$ python track_v5.py --source 0 --yolo-weights weights/yolov5n.pt --img 640
yolov5s.pt
yolov5m.pt
yolov5l.pt
yolov5x.pt --img 1280
...
```
### Yolov7
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
```bash
$ python track_v7.py --source 0 --yolo-weights weights/yolov7-tiny.pt --img 640
yolov7.pt
yolov7x.pt
yolov7-w6.pt
yolov7-e6.pt
yolov7-d6.pt
yolov7-e6e.pt
...
```
### StrongSORT
The above applies to StrongSORT models as well. Choose a ReID model based on your needs from this ReID [model zoo](https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO)
```bash
$ python track_v*.py --source 0 --strong-sort-weights osnet_x0_25_market1501.pt
osnet_x0_5_market1501.pt
osnet_x0_75_msmt17.pt
osnet_x1_0_msmt17.pt
...
```
## Filter tracked classes
By default the tracker tracks all MS COCO classes.
If you only want to track persons I recommend you to get [these weights](https://drive.google.com/file/d/1gglIwqxaH2iTvy6lZlXuAcMpd_U0GCUb/view?usp=sharing) for increased performance
```bash
python track_v*.py --source 0 --yolo-weights weights/v*.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
```bash
python track_v*.py --source 0 --yolo-weights weights/v*.pt --classes 16 17 # tracks cats and dogs, only
```
### Counter
#### get realtime counts of every tracking objects without any rois or any line interctions
```bash
$ python track_v*.py --source test.mp4 -yolo-weights weights/v*.pt --save-txt --count --show-vid
```
### Draw Object Trajectory 
[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
```bash
$ python track_v*.py --source test.mp4 -yolo-weights weights/v*.pt --save-txt --count --show-vid --draw
```
[Here](https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/) 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.
## MOT compliant results
Can be saved to your experiment folder `runs/track/