MOT using deepsort and yolov3 with pytorch
MIT License
Changes
Changes
Futher improvement direction
Added resnet network to the appearance feature extraction network in the deep folder
Fixed the NMS bug in the preprocessing.py
and also fixed covariance calculation bug in the kalmen_filter.py
in the sort folder
Added YOLOv5 detector, aligned interface, and added YOLOv5 related yaml configuration files. Codes references this repo: YOLOv5-v6.1.
The train.py
, val.py
and detect.py
in the original YOLOv5 were deleted. This repo only need yolov5x.pt.
demo/demo2.gif
.train.py
, validation.py
and predict.py
were deleted. This repo only need maskrcnn_resnet50_fpn_coco.pth.nn.parallel.DistributedDataParallel
in PyTorch to support multiple GPUs training.train.py
and train_multiGPU.py
.Updated README.md
for previously updated content(#Update(23-05-2024) and #Update(28-05-2024)).
Any contributions to this repository is welcome!
This is an implement of MOT tracking algorithm deep sort. 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 and the detector used in PAPER is FasterRCNN , and the original source code is HERE. However in original code, the CNN model is implemented with tensorflow, which I'm not familier with. SO I re-implemented the CNN feature extraction model with PyTorch, and changed the CNN model a little bit. Also, I use YOLOv3 to generate bboxes instead of FasterRCNN.
pip install -r requirements.txt
for user in china, you can specify pypi source to accelerate install like:
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
git clone [email protected]:ZQPei/deep_sort_pytorch.git
# if you use YOLOv3 as detector in this repo
cd detector/YOLOv3/weight/
wget https://pjreddie.com/media/files/yolov3.weights
wget https://pjreddie.com/media/files/yolov3-tiny.weights
cd ../../../
# if you use YOLOv5 as detector in this repo
cd detector/YOLOv5
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt
or
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt
cd ../../
# if you use Mask RCNN as detector in this repo
cd detector/Mask_RCNN/save_weights
wget https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
cd ../../../
# if you use original model in PAPER
cd deep_sort/deep/checkpoint
# download ckpt.t7 from
https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this folder
cd ../../../
# if you use resnet18 in this repo
cd deep_sort/deep/checkpoint
wget https://download.pytorch.org/models/resnet18-5c106cde.pth
cd ../../../
cd detector/YOLOv3/nms
sh build.sh
cd ../../..
Notice:
If compiling failed, the simplist way is to **Upgrade your pytorch >= 1.1 and torchvision >= 0.3" and you can avoid the troublesome compiling problems which are most likely caused by either gcc version too low
or libraries missing
.
This library supports bagtricks, AGW and other mainstream ReID methods through providing an fast-reid adapter.
to prepare our bundled fast-reid, then follow instructions in its README to install it.
Please refer to configs/fastreid.yaml
for a sample of using fast-reid. See Model Zoo for available methods and trained models.
This library supports Faster R-CNN and other mainstream detection methods through providing an MMDetection adapter.
to prepare our bundled MMDetection, then follow instructions in its README to install it.
Please refer to configs/mmdet.yaml
for a sample of using MMDetection. See Model Zoo for available methods and trained models.
Run
git submodule update --init --recursive
usage: deepsort.py [-h]
[--fastreid]
[--config_fastreid CONFIG_FASTREID]
[--mmdet]
[--config_mmdetection CONFIG_MMDETECTION]
[--config_detection CONFIG_DETECTION]
[--config_deepsort CONFIG_DEEPSORT] [--display]
[--frame_interval FRAME_INTERVAL]
[--display_width DISPLAY_WIDTH]
[--display_height DISPLAY_HEIGHT] [--save_path SAVE_PATH]
[--cpu] [--camera CAM]
VIDEO_PATH
# yolov3 + deepsort
python deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3.yaml
# yolov3_tiny + deepsort
python deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml
# yolov3 + deepsort on webcam
python3 deepsort.py /dev/video0 --camera 0
# yolov3_tiny + deepsort on webcam
python3 deepsort.py /dev/video0 --config_detection ./configs/yolov3_tiny.yaml --camera 0
# yolov5s + deepsort
python deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov5s.yaml
# yolov5m + deepsort
python deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov5m.yaml
# mask_rcnn + deepsort
python deepsort.py [VIDEO_PATH] --config_detection ./configs/mask_rcnn.yaml --segment
# fast-reid + deepsort
python deepsort.py [VIDEO_PATH] --fastreid [--config_fastreid ./configs/fastreid.yaml]
# MMDetection + deepsort
python deepsort.py [VIDEO_PATH] --mmdet [--config_mmdetection ./configs/mmdet.yaml]
Use --display
to enable display image per frame.
Results will be saved to ./output/results.avi
and ./output/results.txt
.
All files above can also be accessed from BaiduDisk! linker:BaiduDisk passwd:fbuw
Check GETTING_STARTED.md to start training progress using standard benchmark or customized dataset.