Online multi-object tracking via robust collaborative model and sample selection (RCMSS)

This repository includes a Matlab implementation of the RCMSS method in [1] Webpage.

Abstract:

The past decade has witnessed significant progress in object detection and tracking in videos. In this paper, we present a collaborative model between a pre-trained object detector and a number of single-object online trackers within the particle filtering framework. For each frame, we construct an association between detections and trackers, and treat each detected image region as a key sample, for online update, if it is associated to a tracker. We present a motion model that incorporates the associated detections with object dynamics. Furthermore, we propose an effective sample selection scheme to update the appearance model of each tracker. We use discriminative and generative appearance models for the likelihood function and data association, respectively. Experimental results show that the proposed scheme generally outperforms state-of-the-art methods.

Keywords: Multi-object tracking, particle filter, collaborative model, sample selection, sparse representation

Demo:

The code can be tested on the Datasets/PETS2009/S2_L1 test sequence [7] by running the Matlab file named, Version1.0/MultiObjectTrackingMain.m.

Qualitative Results:

The following Youtube video includes a sample qualitative results of RCMSS.

More details are available in the paper in [1] and its Webpage.

Code dependencies:

1. P. Dollár Toolbox in [3]
2. The pre-trained pedestrian detector of P. Dollár et al. in [4]
3. The development kit of the PASCAL Visual Object Classes Challenge 2005 [5]
4. 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.

Citations:

@article{naiel2017RCMSS,
  title={Online multi-object tracking via robust collaborative model and sample selection},
  author={Naiel, Mohamed A and Ahmad, M Omair and Swamy, MNS and Lim, Jongwoo and Yang, Ming-Hsuan},
  journal={Computer Vision and Image Understanding},
  volume={154},
  pages={94--107},
  year={2017},
  publisher={Elsevier}
}

@inproceedings{naiel2014online,
  title={Online multi-person tracking via robust collaborative model},
  author={Naiel, Mohamed A and Ahmad, M Omair and Swamy, MNS and Wu, Yi and Yang, Ming-Hsuan},
  booktitle={2014 IEEE International Conference on Image Processing (ICIP)},
  pages={431--435},
  year={2014},
  organization={IEEE}
}

---

References:

• [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.