基于Pytorch+YOLOv5+SlowFast实现的视频流实时动作检测算法-支持多目标的跟踪检测源码+项目说明.zip

# Yolov5+SlowFast: Realtime Action Detection ### A realtime action detection frame work based on PytorchVideo. #### Here are some details about our modification: - we choose yolov5 as an object detector instead of Faster R-CNN, it is faster and more convenient - we use a tracker(deepsort) to allocate action labels to all objects(with same ids) in different frames - our processing speed reached 24.2 FPS at 30 inference batch size (on a single RTX 2080Ti GPU) > Relevant infomation: [FAIR/PytorchVideo](https://github.com/facebookresearch/pytorchvideo); [Ultralytics/Yolov5](https://github.com/ultralytics/yolov5) #### Demo comparison between original(<-left) and ours(->right). <img src="./demo/ava_slowfast.gif" width="400" /><img src="./demo/yolov5+slowfast.gif" width="400" /> #### Update Log: - 2023.03.31 fix some bugs(maybe caused by yolov5 version upgrade), support real time testing(test on camera or video stearm). - 2022.01.24 optimize pre-process method(no need to extract video to image before processing), faster and cleaner. ## Installation 1. clone this repo: ``` git clone https://github.com/wufan-tb/yolo_slowfast cd yolo_slowfast ``` 2. create a new python environment (optional): ``` conda create -n {your_env_name} python=3.7.11 conda activate {your_env_name} ``` 3. install requiments: ``` pip install -r requirements.txt ``` 4. download weights file(ckpt.t7) from [[deepsort]](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6) to this folder: ``` ./deep_sort/deep_sort/deep/checkpoint/ ``` 5. test on your video/camera/stream: ``` python yolo_slowfast.py --input {path to your video/camera/stream} ``` The first time execute this command may take some times to download the yolov5 code and it's weights file from torch.hub, keep your network connection. set `--input 0` to test on your local camera, set `--input {stream path, such as "rtsp://xxx" or "rtmp://xxxx"}` to test on viewo stream. ## References Thanks for these great works: [1] [Ultralytics/Yolov5](https://github.com/ultralytics/yolov5) [2] [ZQPei/deepsort](https://github.com/ZQPei/deep_sort_pytorch) [3] [FAIR/PytorchVideo](https://github.com/facebookresearch/pytorchvideo) [4] AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions. [paper](https://arxiv.org/pdf/1705.08421.pdf) [5] SlowFast Networks for Video Recognition. [paper](https://arxiv.org/pdf/1812.03982.pdf) ## Citation If you find our work useful, please cite as follow: ``` { yolo_slowfast, author = {Wu Fan}, title = { A realtime action detection frame work based on PytorchVideo}, year = {2021}, url = {\url{https://github.com/wufan-tb/yolo_slowfast}} } ``` ### Stargazers over time [](https://starchart.cc/wufan-tb/yolo_slowfast)