A multi object tracking Library Based on tensorrt

Related tags

Artificial Intelligence detection tensorrt multiple-object-tracking
Overview

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。

Reference

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/

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!

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Comments
  • About your jde model

    About your jde model

    I'd like to know about the difference between your jde model and the official model in which the 3 * (5 + classes_num) + embedding dimensions feature directly from the last same convolution simultaneously.

    opened by Royzon 3
  • ask for the docker image of this project

    ask for the docker image of this project

    hello, your project is helpful! thanks a lot! however, I use TensorRT >= 7.1 with CUDA 10.2, and your project could not run, because the version not match. could you provide a docker image for your project, it will help a lot!

    opened by marsplant 0
  • 请问处理速度如何？

    请问处理速度如何？

    首先，感谢您共享了代码。

    关于CSTrack算法，我比较感兴趣转码后的处理速度如何。

    这个算法，我最初也打算用TRT加速推理。只是在CCN模块遇到硬伤AdaptiveAveragePooling2D无法转成onnx。因此，我将这个模型转成JIT script，并用libtorch去推理，包括后处理部分卡尔曼滤波等这些都是直接用libtorch去做矩阵计算。

    遗憾的是算法输入1333*800比较大， libtorch gpu推理速度 跟 pytorch 差不多，并没有明显提速。整个算法在数据集MOT17-03 测试集 下大约是5fps（i7 7700+1060显卡）。 001423 我查阅了 一下 libtorch 推理速度不快的原因，貌似也有大佬遇到同样的问题。 https://zhuanlan.zhihu.com/p/363319763

    因此，我想大致了解一下去除SAAN 和CCN后，用TRT处理速度是多少？对于这个算法的加速推理和计算还有更好的建议吗？谢谢

    opened by sunxirui310 1
  • Dead loop in function void solve(Matrix<Data> &m) of munkres.h file

    Dead loop in function void solve(Matrix &m) of munkres.h file

    There is a "while" section in the function "void solve(Matrix &m)", In some situations, the function "int step2()" never return 0, so the "while" is caught in dead loop.

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