Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking (IJCV 2018)

Chao Ma, Jia-Bin Huang, Xiaokang Yang, and Ming-Hsuan Yang

Project Webpage https://sites.google.com/site/chaoma99/cf-lstm

Long-Term Correlation Tracking (CVPR 2015)

Chao Ma, Xiakang Yang, Chongyang Zhang, and Ming-Hsuan Yang

Note that this is LCT2.0, we made partial improvements in this version, where the randn fern classifier in LCT1.0 (CVPR 2015) is replaced by an SVM classifier, and the color channels are quantized as four bits for feature.

Quickstart

1. Unzip code or clone it from https://github.com/chaoma99/lct-tracker.git

2. Add the vl_feat toolbox in your current path http://www.vlfeat.org/

3. If you don't have the object tracking benchmark (OTB) dataset yet, run 'download_videos.m' (it will automatically download the OTB-100 sequences)

4. Run utility/compile.m to comile calcIIF.cpp and im2colstep.c. These files are tested with opencv3.0. Notice the compatibility issue if your opencv version is earlier than 3.0

5. The source files "assignToBins1.c", "gradientMex.cpp", 'imResample.cpp' are from Pitor Dollar's toolbox. If the compiled files do not work on your system, get it from http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html

Introduction

The script 'run_tracker' without parameters is to choose a video and test the proposed tracker. The start interface is 'run_tracker'.

We also provide the interface 'run_lct2' to reproduce our results on OBT tracking benchmark

run_tracker Without any parameters, will ask you to choose a video, and track the objects, and show the results in an interactive figure. Press 'Esc' to stop the tracker early. You can navigate the video using the scrollbar at the bottom.

run_tracker VIDEO Allows you to select a VIDEO by its name. 'all' will run all videos and show average statistics. 'choose' will select one interactively.

For the actual tracking code, check out the 'tracker_lct' function.

Though it's not required, the code will make use of the MATLAB Parallel Computing Toolbox automatically if available.

References

[1] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, "High-Speed Tracking with Kernelized Correlation Filters", TPAMI 2015.

[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, "Exploiting the Circulant Structure of Tracking-by-detection with Kernels", ECCV 2012.

[3] Y. Wu, J. Lim, M.-H. Yang, "Online Object Tracking: A Benchmark", CVPR 2013. Website: http://visual-tracking.net/

[4] P. Dollar, "Piotr's Image and Video Matlab Toolbox (PMT)". Website: http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html