Browse > Article
http://dx.doi.org/10.6109/jkiice.2012.16.4.812

Multiple Pedestrians Tracking using Histogram of Oriented Gradient and Occlusion Detection  

Jeong, Joon-Yong (대전대학교 정보통신공학과)
Jung, Byung-Man (대전대학교 정보통신공학과)
Lee, Kyu-Won (대전대학교 정보통신공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.16, no.4, 2012 , pp. 812-820 More about this Journal
Abstract
In this paper, multiple pedestrians tracking system using Histogram of Oriented Gradient and occlusion detection is proposed. The proposed system is applicable to Intelligent Surveillance System. First, we detect pedestrian in a image sequence using pedestrian's feature. To get pedestrian's feature, we make block-histogram using gradient's direction histogram based on HOG(Histogram of Oriented Gradient), after that a pedestrian region is classified by using Linear-SVM(Support Vector Machine) training. Next, moving objects are tracked by using position information of the classified pedestrians. And we create motion trajectory descriptor which is used for content based event retrieval. The experimental results show that the proposed method is more fast, accurate and effective than conventional methods.
Keywords
Pedestrian Tracking; Histogram of Oriented Gradient; Motion Descriptor; Multiple Object Tracking;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Mählisch, M. Oberländer, O. Löhlein, D. Gavrila and W. Ritter, A multiple detector approach to low-resolution for pedestrian recognition, Procs. of the IEEE Intelligent Vehicles Symposium 2005, pp.23-28, 2005.
2 D. M. Gavrila, Pedestrian Detection from a Moving Vehicle, Procs. of European Conference on Computer Vision, Vol. 2, pp. 37-49, 2000.
3 C. Curio, J. Edelbrunner, T. Kalinke, C. Tzomakas and Werner von Seelen, Walking Pedestrian Recognition, IEEE Trans. on Intelligent Transportation Systems, Vol. 1, No. 3, pp.155-163, 2000.   DOI   ScienceOn
4 N. Dalal and B. Triggs, Histogram of Oriented Gradient for Human Detection, In CVPR, Vol.2, pp. 1515-1522, September 2009.
5 C. Papageorgius and T. Poggio, A Trainable system for object detection, International Conference on Computer, Vol. 38, No.1 pp. 15-33, 2000.
6 V. Prisacariu and I. Reid, fastHOG - a real-time GPU implementation of HOG, Technical Report 2310/09, University of Oxford, 2009.
7 M. Bertozzi, A. Broggi, A. Fascioli, A. Tibaldi, R. Chapius and F. Cahusse, Pedestrian localization and tracking system with Kalman filtering, IEEE Intelligent Vehicles Symposium 2004, pp. 584-589, June 2004.
8 M. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier and L. Van Gool, Robust tracking-by-detection using a detector confidence particle filter, In ICCV, pp. 1515-1522, September, 2009.
9 S. Stalder, H. Grabner and L. J. V. Gool, Cascaded confidence filtering for improved tracking-bydetection, In ECCV (1), vol. 6311 of LNCS, pp. 605-619, Springer, 2010.
10 K. Bernardin and R. Stiefelhagen, Evaluating multiple object tracking performace: the CLEAR MOT metrics, IN EURASIP JIVP, 2008.
11 M. Valera and S. A. Velastin, Intelligent distributed surveillance systems: a review, Proc. Of Vision '05, Image and Signal Processing, Vol. 152, pp. 192-204, 2005.   DOI   ScienceOn
12 Markus Enzweiler and M. Gavrila, Monocular Pedestrian Detection: Survey and Experiments, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 31, No. 12, December 2009.
13 Lim, J. S. and Kim, K. H. Multiple Pedestrians Detection and Tracking using Color Information form Moving Camera, Journal of KIPS : Korea Information and Applications B, Vol. 11-B, No.3, pp.317-326, 2004.
14 A. Broggi, M. Bertozzi and A. Fascioli, Shape based Pedestrian Detection, Procs. of the IEEE Intelligent Vehicle Symposium 2000, pp.215-220, 2000.