/deep_sort_yolov3

Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Introduction

Thanks for these projects, this work now is support tiny_yolo v3 but only for test, if you want to train you can either train a model in darknet or in the second following works. It also can tracks many objects in coco classes, so please note to modify the classes in yolo.py. besides, you also can use camera for testing.

https://github.com/nwojke/deep_sort

https://github.com/qqwweee/keras-yolo3

https://github.com/Qidian213/deep_sort_yolov3

Quick Start

  1. Download YOLOv3 or tiny_yolov3 weights from YOLO website.Then convert the Darknet YOLO model to a Keras model. Or use what i had converted https://drive.google.com/file/d/1uvXFacPnrSMw6ldWTyLLjGLETlEsUvcE/view?usp=sharing (yolo.h5 model file with tf-1.4.0) , put it into model_data folder

  2. Run YOLO_DEEP_SORT with cmd :

    python demo.py

  3. (Optional) Convert the Darknet YOLO model to a Keras model by yourself:

 please download the weights at first from yolo website. 
 python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5

Dependencies

The code is compatible with Python 2.7 and 3. The following dependencies are needed to run the tracker:

NumPy
sklean
OpenCV

Additionally, feature generation requires TensorFlow-1.4.0.

Note

Model file model_data/mars-small128.pb need by deep_sort had convert to tensorflow-1.4.0

Test only

Speed : when only run yolo detection about 11-13 fps , after add deep_sort about 11.5 fps (GTX1060 6G)

Test result video : https://www.bilibili.com/video/av23500163/ generated by this project