YoloV5_JDE_TensorRT_for_Track

Introduction

A multi object detect and track Library Based on tensorrt

一个基于TensorRT的多目标检测和跟踪融合算法库，可以同时支持行人的多目标检测和跟踪，当然也可以仅仅当检测库使用。

本算法的主框架采用了JDE+deepsort结构，其中由JDE算法检测出人的坐标与其对应的外观特征，然后基于deepsort的方法进行目标与运动轨迹的匹配。 JDE中的检测框架则采用了YOLOV5 L 的模型结构。

CSTrack3_0.yaml为本网络的模型结构，模型训练的代码大部分借鉴了CSTrack原文作者的开源项目，这里不再开源，大家有兴趣可以阅读CSTrack论文。 需要注意的是，本项目由于追求速度将CStrack的CCN和SAAN模块改成了JDE模块，也就是直接在anchor上提取reid特征并没有进行detect和reid的解耦模块。 如果读者需要的话可以自行修改，这样可以提升IDswich方面的性能。

Reference

• JDE: https://arxiv.org/pdf/1909.12605v1.pdf
• CSTrack: https://arxiv.org/pdf/2010.12138.pdf
• YOLOV5: https://github.com/ultralytics/yolov5
• DeepSort: https://arxiv.org/pdf/1703.07402.pdf

Requirements

• ubuntu 18.04 for x86
• gcc/g++ >= 7.5.0
• opencv >= 3.4.8
• cuda >=10.0 cudnn >= 7.6
• tensorRT >= 7.0.0
• (Optional) ffmpeg (used in the video demo)

How to build and run

• modify track/CMakeLists.txt Change opencv and tensorRT to your local directory
• modify demo/CMakeLists.txt Change opencv and tensorRT to your local directory
• modify demo/src/main.cpp Change video path to your local directory
• sh make.sh
• cd demo/build
• ./itest

How to convert to tensort gie file

• cd PytorchToTensorRTGIE
• modify CMakeLists.txt Change opencv and tensorRT to your local directory
• download jde.wts file
• cd build
• cmake ..
• make
• ./yolov5 -s
• #Verify detect results
• ./yolov5 -d ../sample/

Model

• TensorRT GIE Model File: [Baidu] key: 6yc6.

Download the model and put it to /weight/

• .wts File: [Baidu] key: c30n.

Download the model and put it to /PytorchToTensorRTGIE/

Video Demo

Acknowledgement

A large portion of code is borrowed from wang-xinyu/tensorrtx and sephirothhua/DeepSort_yoloV3-HOG_feature and , many thanks to their wonderful work!