跟踪算法-使用YOLOv9+ByteTracker实现目标跟踪算法-优质算法项目实战.zip

<h1 align="center"><span>YOLOv9 + ByteTracker</span></h1> This project integrates YOLOv9 and ByteTracker for real-time, TensorRT-optimized object detection and tracking, extending the existing implementation. <p align="center" margin: 0 auto;> <img src="assets/demo.gif" width="360px" /> <img src="assets/highway.gif" width="360px" /> </p> ## Usage - CPP(TensorRT): ``` shell cd build/release yolov9-bytetrack-trt.exe yolov9-c.engine test.mp4 # the video path ``` - Python(ONNX): ``` shell cd python python yolov9_bytetrack.py --model yolov9-c-converted.onnx --video_path test_video.mp4 --output_video_path result.mp4 ``` ## What is next? - [ ] Python(TensorRT) ## Setup **Python:** ``` shell cd python pip install -r requirement.txt ``` **CPP:** Refer to our [docs/INSTALL.md](https://github.com/spacewalk01/yolov9-bytetrack-tensorrt/blob/main/docs/INSTALL.md) for detailed installation instructions. Note that Bytetracker is directly integrated without any modification. ## Requirement - TensorRT - CUDA, CudaNN - Eigen 3.3 - C++ compiler with C++17 or higher support - CMake 3.14 or higher - OpenCV