2021.06.06 Pytorch version: https://github.com/HansRen1024/C-OF. Code is released (20210816).
2021.05.10 Pytorch version will be released soon.
2019.01.25 UPDATE
Some guys are not familiar with ncnn, and compile this repo with an error of could not find net.h. Please move to https://github.com/Tencent/ncnn to find what is ncnn and how to install it. Download the version we used from https://github.com/Tencent/ncnn/archive/refs/tags/20180830.zip.
2018.12.20 IMPORTANT UPDATE
I extremely optimized the code, all useless contents were removed. Right now, tracking speed is approximate 3ms. Besides, I deleted initialization funtion and OpenCV 3.x is supported now.
RK3399 20+ ms/frame
Face-Tracking-Using-Optical-Flow-and-CNN
I optimized OpenTLD making it run faster and better for face tracking.
This version of TLD is faster and more stable than that in OpenCV. I delete some funtions to make it run faster. What is more, use RNet to judge the face that TLD produced to avoid TLD tracking a wrong target. In order to get a stable bounding box, I fix the width and height that MTCNN provides. Running time on my PC(Intelยฎ Xeon(R) CPU E5-2673 v3 @ 2.40GHz ร 48) is about 16ms(MTCNN, ncnn), 30ms(TLD initialization), 10ms(TLD tracking) on an image of 320*240 resolution. Besides, MTCNN can be replaced by PCN or any other face/object detection algorithms.
ไธญๆไป็ปๅฐๅ๏ผhttps://blog.csdn.net/renhanchi/article/details/85089265
Installing
OpenCV 2.4.X is required!(Now OpenCV 3.x is supported)
Install ncnn firstly, and reset ncnn's include and lib pathes in CMakeLists.txt.
mkdir build
cd build
cmake ..
make
cd ..
./demoExamples
References
https://github.com/Tencent/ncnn