视频图matlab代码-Tracking_by_detection:系统使用yolov3/KCF＆DSST/KF来检测，跟踪和估

# Tracking_by_detection A Python3-based system use yolov3/KCF&DSST/KF to detect,tracking and estimate the target（people and cars） location. 这是我的本科毕设项目，主要通过YOLOv3识别，利用KCF-DSST-APCE完成抗遮挡的尺度变化跟踪，并用卡尔曼滤波估计位置。 # 视频效果/video ： https://www.bilibili.com/video/bv1Xa4y1Y7Ue # Test environment Intel Core i5-8300H CPU@2.30GHz Nvidia GTX 1050Ti Ubuntu 18.04LTS Python3 OPENCV3.4.4 # Requirements CUDA9.0 CUDNN7 Python3 OPENCV Numpy Numba # Code structure yolo.py:detect fhog.py:give fhog feature tracker.py:track run.py:basic framework run2.py:add more visualization work for some video # Cites and Others' codes' citation @article{redmon2018yolov3, title={Yolov3: An incremental improvement}, author={Redmon, Joseph and Farhadi, Ali}, journal={arXiv preprint arXiv:1804.02767}, year={2018} } @article{henriques2014high, title={High-speed tracking with kernelized correlation filters}, author={Henriques, Jo{\~a}o F and Caseiro, Rui and Martins, Pedro and Batista, Jorge}, journal={IEEE transactions on pattern analysis and machine intelligence}, volume={37}, number={3}, pages={583--596}, year={2014}, publisher={IEEE} } @inproceedings{danelljan2014accurate, title={Accurate scale estimation for robust visual tracking}, author={Danelljan, Martin and H{\"a}ger, Gustav and Khan, Fahad and Felsberg, Michael}, booktitle={British Machine Vision Conference, Nottingham, September 1-5, 2014}, year={2014}, organization={BMVA Press} } @inproceedings{wang2017large, title={Large margin object tracking with circulant feature maps}, author={Wang, Mengmeng and Liu, Yong and Huang, Zeyi}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={4021--4029}, year={2017} } The YOLOv3 part is based on yehengcheng's work @ https://github.com/yehengchen/Object-Detection-and-Tracking The KCF and DSST part is based on ryanfwy's work @ https://github.com/ryanfwy/KCF-DSST-py # Install and use ## First you should clone this repository and get yolo weight: > git clone https://github.com/sjtuzyz/Tracking_by_detection.git go to yehengcheng's work to get the yolo weight or learn how to transfer it. ## Then change some part of code to slove your task ### Go to yolo.py line 122 > if predicted_class != 'person': The class should be changed to person or car or whatever you want.About the yolo network's training and convert,please check yehengcheng's work. ### Go to tracker.py line 463 ```python if (APCE>0.12):#25 oc1.mp4 0.02 oc2.mp4 x = self.getFeatures(image, 0, 1.0) self.train(x, self.interp_factor) #x = self.getFeatures(image, 0, 1.0) #self.train(x, self.interp_factor) ``` If you want to use APCE to let the system more robust to occlusions ,just change 0.12 to a threshold of your test video, which will be print in the terminal. ### Go to run.py(or run2.py and find where to change by yourself)line 35 > cap = cv2.VideoCapture('./outputcar1.mp4') Change to .mp4 file to test your own video. ### Go to run.py(or run2.py and find where to change by yourself)line 50-78 and 109-136 I use similar geometric relations to calculate the distance,which means you need to get the Intrinsic Matrix K by Matlab or any other code and target's height or width first.Then just change the parameters to yours. ### Go to run.py(or run2.py and find where to change by yourself)line 205 > c = cv2.waitKey(1) & 0xFF change the waitkey to get the frame played slower.