This is an implementation of MOT tracking algorithm deep sort cplusplus code. Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. This CNN model is indeed a RE-ID model. The idea of deepsort is adopted in object tracking.
We use yolov7 model as the object detector. And the feature extractor is RE-ID model as which fast-reid is used. The purpose of using these lightweight models is to ensure the real-time efficiency of video processing. The model inference base on TensorRT engine. It also supports yolov5 as a detector.
Object detection
- YOLOV7
- YOLOV5s
ReID
- fast-reid(mobilenet-v2)
Expand
yolov7_deepsort_tensorrt/
|-- build
|-- configs
|-- depends
| `-- yaml-cpp
| |-- include
| | `-- yaml-cpp
| | |-- contrib
| | `-- node
| | `-- detail
| `-- libs
|-- includes
|-- samples
|-- scripts
|-- src
`-- weights
Expand
OpenCV >= 4.1.1
CUDA Version: 11.1
CUDNN Version: 8.1.0
Tensorrt: 7.2.2
Yaml: 0.7.0
Expand
0. Check all dependencies installed
see Dependencies for more detail.
1. Clone this repository and models
1.1 Get this repository
git clone https://github.com/xuarehere/yolov7_deepsort_tensorrt.git
1.2 Get the models
cd scripts/
bash scripts/get_weight.sh
In addition, you could get the model from the releases. Then, the step2 and the step3 could be skipped Optionally.
2. Get detector parameters(Optionally)
cd weights
# Get model parameters
cd ../
yolov7
Please use the unofficial project unofficial-yolov7 to get the ONNX model. Run the following command
git clone https://github.com/linghu8812/yolov7.git
cd yolov7
python export.py --weights ./weights/yolov7.pt --simplify --grid
3. Get ReID parameters(Optionally)
cd weights
# Get model parameters
cd ../
Please use the official project fast-reid to get the ONNX model. Run the following command
https://github.com/JDAI-CV/fast-reid.git
python3 tools/deploy/onnx_export.py --config-file configs/Market1501/mgn_R50-ibn.yml --name mgn_R50-ibn --output outputs/onnx_model --batch-size 32 --opts MODEL.WEIGHTS market_mgn_R50-ibn.pth
4. Prepare video for inference
We provide a default video for inference(001.avi). You could change it with yours.
5. Buid project
5.1 Use build.sh
cd scripts
bash build.sh
If the directory ./build exists, you want to remove it and build it again, please use the command:
cd scripts
bash build.sh rm
5.2 Build it manually
mkdir build
cd scripts
cd ../build/ && cmake .. && make -j$(nproc) && cd -
6. Run demo
cd scripts
bash yolov7_deepsort.sh
Expand
- yolov7: https://github.com/WongKinYiu/yolov7
- yolovx: https://github.com/xuarehere/yolovx_deepsort_pytorch
- yolov5: https://github.com/ultralytics/yolov5
- yolov5_fastreid_deepsort_tensorrt:https://github.com/linghu8812/yolov5_fastreid_deepsort_tensorrt
- FastReID: A Pytorch Toolbox for General Instance Re-identification: https://arxiv.org/abs/2006.02631
- fast-reid: https://github.com/JDAI-CV/fast-reid
- Simple Online and Realtime Tracking: https://arxiv.org/abs/1602.00763
- sort-cpp: https://github.com/mcximing/sort-cpp
- Simple Online and Realtime Tracking with a Deep Association Metric: https://arxiv.org/abs/1703.07402
- tensorrt_inference: https://github.com/linghu8812/tensorrt_inference
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent