- Download YOLOv3 weights from YOLO website.
- Convert the Darknet YOLO model to a Keras model.
- Run YOLO_DEEP_SORT
wget https://pjreddie.com/media/files/yolov3.weights python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5 python demo.py
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.
file model_data/mars-small128.pb had convert to tensorflow-1.4.0
file model_data/yolo.h5 is to large to upload ,so you need convert it from Darknet Yolo model to a keras model by yourself
yolo.h5 model can download from https://drive.google.com/file/d/1uvXFacPnrSMw6ldWTyLLjGLETlEsUvcE/view?usp=sharing , use tensorflow1.4.0
use : 'video_capture = cv2.VideoCapture('path to video')' use a video file or 'video_capture = cv2.VideoCapture(0)' use camera
speed : when only run yolo detection about 11-13 fps , after add deep_sort about 11.5 fps
test video : https://www.bilibili.com/video/av23500163/
From the issue https://github.com/Qidian213/deep_sort_yolov3/issues/7 , it can tracks cars, birds and trucks too and performs well .