Video target tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in target tracking for nonlinear and non-Gaussian estimation problems. Although most existing algorithms are able to track targets well in controlled environments, it is often difficult to achieve automated and robust tracking of pedestrians in video sequences if there are various changes in target appearance or surrounding illumination. To surmount these difficulties, this paper presents multitarget tracking of pedestrians in video sequences based on particle filters. In order to improve the efficiency and accuracy of the detection, the algorithm firstly obtains target regions in training frames by combining the methods of background subtraction and Histogram of Oriented Gradient (HOG) and then establishes discriminative appearance model by generating patches and constructing codebooks using superpixel and Local Binary Pattern (LBP) features in those target regions. During the process of tracking, the algorithm uses the similarity between candidates and codebooks as observation likelihood function and processes severe occlusion condition to prevent drift and loss phenomenon caused by target occlusion. Experimental results demonstrate that our algorithm improves the tracking performance in complicated real scenarios. 1. Introduction Video target tracking is an important research field in computer vision for its wide range of application demands and prospects in many industries, such as military guidance, visual surveillance, visual navigation of robots, human-computer interaction and medical diagnosis [1–3], and so forth. The main task of target tracking is to track one or more mobile targets in video sequences so that the position, velocity, trajectory, and other parameters of the target can be obtained. Two main tasks needs to be completed by moving target tracking during the processing procedure: the first one is target detection and classification which detects the location of relevant targets in the image frames; the second one is the relevance of the target location of consecutive image frames, which identifies the target points in the image and determines their location coordinates, thus to determine the trajectory of the target as time changes. However, automated detection and tracking of pedestrians in video sequences is still a challenging task because of following reasons [4]. (1) Large intraclass variability which refers to various changes in appearance of pedestrians due to different poses, clothing, viewpoints,
References
[1]
B. Babenko, S. Belongie, and M. H. Yang, “Visual tracking with online multiple instance learning,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, pp. 983–990, June 2009.
[2]
J. Kwon and K. M. Lee, “Visual tracking decomposition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 1269–1276, June 2010.
[3]
A. Makris, D. Kosmopoulos, S. Perantonis, and S. Theodoridis, “A hierarchical feature fusion framework for adaptive visual tracking,” Image and Vision Computing, vol. 29, no. 9, pp. 594–606, 2011.
[4]
B. Leibe, E. Seemann, and B. Schiele, “Pedestrian detection in crowded scenes,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 878–885, June 2005.
[5]
H. Zhou, Y. Yuan, and C. Shi, “Object tracking using SIFT features and mean shift,” Computer Vision and Image Understanding, vol. 113, no. 3, pp. 345–352, 2009.
[6]
D. Comaniciu and V. Ramesh, “Mean shift and optimal prediction for efficient object tracking,” in Proceedings of the International Conference on Image Processing (ICIP '00), pp. 70–73, Vancouver, Canada, September 2000.
[7]
B. O. S. Teixeira, M. A. Santillo, R. S. Erwin, and D. S. Bernstein, “Spacecraft tracking using sampled-data Kalman filters,” IEEE Control Systems Magazine, vol. 28, no. 4, pp. 78–94, 2008.
[8]
M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174–188, 2002.
[9]
S. L. Tang, Z. Kadim, K. M. Liang, and M. K. Lim, “Hybrid blob and particle filter tracking approach for robust object tracking,” in Proceedings of the 10th International Conference on Computational Science (ICCS '10), pp. 2559–2567, June 2010.
[10]
K. Nummiaro, E. Koller-Meier, and L. Van Gool, “An adaptive color-based particle filter,” Image and Vision Computing, vol. 21, no. 1, pp. 99–110, 2003.
[11]
J. Vermaak, A. Doucet, and P. Pérez, “Maintaining multi-modality through mixture tracking,” in Proceedings of the 9th IEEE International Conference on Computer Vision, pp. 1110–1116, October 2003.
[12]
K. Okuma, A. Taleghani, N. De Freitas, et al., “A boosted particle filter: Multitarget detection and tracking,” in Proceedings of the European Conference on Computer Vision, pp. 28–39, 2004.
[13]
A. Vedaldi and S. Soatto, “Quick shift and kernel methods for mode seeking,” in Proceedings of the European Conference on Computer Vision, pp. 705–718, 2008.
[14]
A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, “TurboPixels: fast superpixels using geometric flows,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2290–2297, 2009.
[15]
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels,” EPFL Technical Report 149300, 2010.
[16]
X. Ren and J. Malik, “Tracking as repeated figure/ground segmentation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8, June 2007.
[17]
S. Wang, H. C. Lu, F. Yang, and M. H. Yang, “Superpixel tracking,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 1323–1330, 2011.
[18]
F. Yang, H. Lu, and Y. W. Chen, “Bag of features tracking,” in Proceedings of the International Conference on Pattern Recognition (ICPR '10), pp. 153–156, August 2010.
[19]
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 886–893, June 2005.
