PJS: Patchwise Joint Sparse Tracking Matlab Framework
PJS is a Matlab framework for visual tracking. For more information about PJS tracker check out the project site. This framework is designed for research purpose only. Please cite [1,2] if you use this framework.
- It includes implementations of state-of-the-art visual trackers including APG , IVT , MIL , MTT  and our proposed tracker PJSM, PJSS [1,2].
- A collection of standard evaluation measures of visual trackers in bin directory.
- A set of m-files for plotting trackers performance.
Install SPAMS  and vlfeat  libraries and add them to MATLAB path. Then run compile.m.
If you need specific compile options, edit compile_mex_code.m from the
Set parameters in "set_param.m" file. Then Install required dataset e.g. from here. Change the folder naming and ground-truth as the sample dataset (trellis) provided in
- imgs: this folder contains all images of video sequence.
- <dataset_name>_gt.txt: each row of this file is the [upper_left_corner_x, upper_left_corner_y, width, height] of the bounding box defining the target.
- <dataset_name>_gtInterv.txt: this file contains a number defining the interval in which the ground truth are available. For example use 1 if ground-truth is available for every frame and 5 if ground-truth is available for every five frame.
Main file for running different trackers are named as main_<tracker_name>.m.
Results are saved in
Implementations of APG, IVT, MIL and MTT are taken from author's site, and only edited to run in our framework.
- Zarezade A., Rabiee H. R., Soltani-Farani A., and Khajenezhad A., “Patchwise Joint Sparse Tracking with Occlusion Detection”, IEEE Transactions on Image Processing (TIP), 2014. download
- Soltani-Farani, Ali, Hamid R. Rabiee, and Ali Zarezade. "Collaborating frames: Temporally weighted sparse representation for visual tracking.", IEEE International Conference on Image Processing (ICIP), 2014. download
- Bao, Chenglong, et al. "Real time robust l1 tracker using accelerated proximal gradient approach." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
- Ross, David A., et al. "Incremental learning for robust visual tracking." International Journal of Computer Vision 77.1-3 (2008): 125-141.
- Babenko, Boris, Ming-Hsuan Yang, and Serge Belongie. "Robust object tracking with online multiple instance learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 33.8 (2011): 1619-1632.
- Zhang, Tianzhu, et al. "Robust visual tracking via multi-task sparse learning." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.