This repository includes a Matlab implementation of the RCMSS method in [1] Webpage.
The code can be tested on the Datasets/PETS2009/S2_L1 test sequence [7] by running the Matlab file named, Version1.0/MultiObjectTrackingMain.m.
The following Youtube video includes a sample qualitative results of RCMSS.
More details are available in paper in [1] and its Webpage.
- P. Dollár Toolbox in [3]
- The pre-trained pedestrian detector of P. Dollár et al. in [4]
- The development kit of the PASCAL Visual Object Classes Challenge 2005 [5]
- The code of sparsity-based tracker presented in [6] Note: For convienance, a copy from the code dependancies, sample dataset and sample quantitative results are stored under Dependencies/, Datasets/ and Quantitative Results/ subfolders, respectively.
- [1] M.A. Naiel, M.O. Ahmad, M.N.S. Swamy, J. Lim, and M.-H. Yang, "Online multi-object tracking via robust collaborative model and sample selection", Computer Vision and Image Understanding, Volume 154, 2017, Pages 94-107. PDF
- [2] M.A. Naiel, M.O. Ahmad, M.N.S. Swamy, Y. Wu, and M.-H. Yang, "Online multi-person tracking via robust collaborative model", 21st IEEE International Conference on Image Processing (ICIP), Paris, France, pp. 431 – 435, Oct. 2014.
- [3] piotr_toolbox_V3.01 "http://vision.ucsd.edu/~pdollar/toolbox/doc/"
- [4] P. Dollár, S. Belongie and P. Perona, "The Fastest Pedestrian Detector in the West", BMVC 2010, Aberystwyth, UK.
- [5] The PASCAL Visual Object Classes Challenge 2005 Development Kit "http://host.robots.ox.ac.uk/pascal/VOC/voc2005/index.html"
- [6] W. Zhong, H. Lu, and M.-H. Yang, “Robust object tracking via sparsity-based collaborative model,” In Proc. Comput. Vis. Pattern Recognit., 2012, pp. 1838–1845.
- [7] J. Ferryman, in: Proc. IEEE Workshop Performance Evaluation of Tracking and Surveillance, 2009.
Copyright 2016 (©) Mohamed A. Naiel all rights reserved.