Vision Based People Tracking System
In this paper we present the design and the implementation of a target tracking system where the target is set to be a moving person in a video sequence. The system can be applied easily as a vision system for mobile robot. The system is composed of two major parts the first is the detection of the person in the video frame using the SVM learning machine based on the “HOG” descriptors. The second part is the tracking of a moving person it’s done by using a combination of the Kalman filter and a modified version of the Camshift tracking algorithm by adding the target motion feature to the color feature, the experimental results had shown that the new algorithm had overcame the traditional Camshift algorithm in robustness and in case of occlusion.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3593110Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 617
 Dalal, N. and Triggs, B. Histograms of oriented gradients for human detection. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR-05), 2005 886–893.
 P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In CVPR, 2008.
 C. H. Lampert, M. B. Blaschko, and T. Hofmann. Beyond sliding windows: Object localization by efficient subwindow search. In CVPR, 2008
 O. Tuzel, F. Porikli, and P. Meer. Human detection via classification on Riemannian manifolds. In CVPR, pages 1–8, 2007
 S. Munder and D. Gavrila. An experimental study on pedestrian classification. IEEE Trans. Pattern Anal. Mach. Intell., 28(11):1863–1868, Nov. 2006
 Wang, X., Han, T. X., & Yan, S. An HOG-LBP human detector with partial occlusion handling. In Computer Vision, 2009 IEEE 12th International Conference on (pp. 32-39). September 2009.
 Veenman, C. Reinders, M., and Backer, E. 2001. Resolving motion correspondence for densely moving points. IEEE Trans. Patt. Analy. Mach. Intell. 23, 1, 54–72
 Serby, D., Koller-Meier, S., and Gool, L. V. 2004. Probabilistic object tracking using multiple features. In IEEE International Conference of Pattern Recognition (ICPR). 184–187
 Comaniciu, D., Ramesh, V., Andmeer, P. 2003. Kernel-based object tracking. IEEE Trans. Patt. Analy. Mach Intell. 25, 564–575
 Yilmaz, A., Li, X., and Shah, M. 2004. Contour based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans. Patt. Analy. Mach. Intell. 26, 11, 1531–1536.
 Cheng, Yizong (August 1995). "Mean Shift, Mode Seeking, and Clustering". IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE) 17 (8): 790–799
 Huang, S., & Hong, J. (2011, April). Moving object tracking system based on camshift and Kalman filter. In Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on (pp. 1423-1426). IEEE
 G. Bradski, and T. Ogiuchi, and M. Higashikubo, “Visual Tracking Algorithm using Pixel-Pair Feature”. International Conference on Pattern Recognition, no. 4, pp. 1808–1811. 2010)
 Y. Ruiguo, and Z. Xinrong, “The Design and Implementation of Face Tracking in Real Time Multimedia Recording System”. IEEE Transaction, no. 3, pp. 1–3. 2009
 E. David, and B. Erich, and K. Daniel, and S. Anselm, “Fast and Robust Camshift Tracking”. IEEE Transaction, no. 8, pp. 1–8. 2010
 Boubou, S., Kouno, A., & Suzuki, E. (2011, December). Implementing Camshift on a Mobile Robot for Person Tracking and Pursuit. In Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on (pp. 682-688). IEEE
 X. Sun and H. Yao, and S. Zhang, “A Refined Particle Filter Method for Contour Tracking”. Visual Communications and Image Processing, no. 8, pp. 1–8. 2010
 Welch, G., & Bishop, G. (1995). An introduction to the Kalman filter
 Salmond, D. (2001, October). Target tracking: introduction and Kalman tracking filters. In Target Tracking: Algorithms and Applications (Ref. No. 2001/174), IEE (pp. 1-1). IET
 Wang, X., & Li, X. (2010, December). The study of MovingTarget tracking based on Kalman-CamShift in the video. In Information Science and Engineering (ICISE), 2010 2nd International Conference on (pp. 1-4). IEEE
 Kovacevic, J., Juric-Kavelj, S., & Petrovic, I. (2011, May). An improved CamShift algorithm using stereo vision for object tracking. In MIPRO, 2011 Proceedings of the 34th International Convention (pp. 707-710). IEEE
 Salhi, A., & Jammoussi, A. Y. (2012). Object tracking system using Camshift, Meanshift and Kalman filter. World Academy of Science, Engineering and Technology
 Li, J., Zhang, J., Zhou, Z., Guo, W., Wang, B., & Zhao, Q. (2011, October). Object tracking using improved Camshift with SURF method. In Open-Source Software for Scientific Computation (OSSC), 2011 International Workshop on (pp. 136-141). IEEE
 Hidayatullah, P., & Konik, H. (2011, July). CAMSHIFT improvement on multi-hue and multi-object tracking. In Electrical Engineering and Informatics (ICEEI), 2011 International Conference on (pp. 1-6). IEEE
 Fahad Fazal Elahi Guraya, Pierre-Yves Bayle and Faouzi Alaya Cheikh, People Tracking via a Modified Camshift Algorithm, DCABES;2009
 Gary Bradski, Adrian Kaehler, “Learning OpenCV", O'Reilly, 2008
 Schiele, B., Andriluka, M., Majer, N., Roth, S., & Wojek, C. (2009, May). Visual people detection: Different models, comparison and discussion. In Proceedings of the IEEE ICRA 2009 workshop on people detection and tracking (Vol. 12)
 Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.
 Bradski, G. R. “Computer Vision Face Tracking for Use in a Perceptual User Interface”. Intel Technology Journal, 2(2), 13-27, 1998