Browse > Article
http://dx.doi.org/10.5573/ieie.2014.51.9.139

Multi-Object Detection and Tracking Using Dual-Layer Particle Sampling  

Jeong, Kyungwon (Dept. of Image Engineering. Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Kim, Nahyun (Dept. of Image Engineering. Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Lee, Seoungwon (Dept. of Image Engineering. Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Paik, Joonki (Dept. of Image Engineering. Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.9, 2014 , pp. 139-147 More about this Journal
Abstract
In this paper, we present a novel method for simultaneous detection and tracking of multiple objects using dual-layer particle filtering. The proposed dual-layer particle sampling (DLPS) algorithm consists of parent-particles (PP) in the first layer for detecting multiple objects and child-particles (CP) in the second layer for tracking objects. In the first layer, PPs detect persons using a classifier trained by the intersection kernel support vector machine (IKSVM) at each particle under a randomly selected scale. If a certain PP detects a person, it generates CPs, and makes an object model in the detected object region for tracking the detected object. While PPs that have detected objects generate CPs for tracking, the rest of PPs still move for detecting objects. Experimental results show that the proposed method can automatically detect and track multiple objects, and efficiently reduce the processing time using the sampled particles based on motion distribution in video sequences.
Keywords
particle filter; object detection; object tracking; particle window;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. Moon, and S. Shin, "Implementation of Inteligent Image Surveilance System based Contex", Journal of The Institute of Electronics Engineers of Korea, vol. 47SP, no. 3, pp. 11-22, May 2010.
2 Y. Chai, S. Shin, K. Chang, and T. Kim, "Real-time user interface using particle filter with integral histogram," IEEE Trans. Consumer Electronics, vol. 56, no. 2, pp. 510-515, May 2010.   DOI   ScienceOn
3 K. Okuma, A. Taleghani, N. Freitas, J. Little, and D. Lowe, "A boosted particle filter: multitarget detection and tracking", Proc. European Conference on Computer Vision, vol. 3021, pp. 28-39, 2004.
4 Y. Rui and Y. Chen, "Better proposal distributions: object tracking using unscented particle filter,"Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 786-793, 2001.
5 H. Grabner, M. Grabner, and H. Bischof, "Real-time tracking via on-line boosting," Proc. British Machine Vision Conference, vol. 1, pp. 47-56, 2006.
6 Z. Chen "Bayesian filtering: From Kalman Filters to particle filters, and beyond," Technical Report McMasters University, Hamilton, 2003.
7 G. Gualdi, A. Prati, R. Cucchiara "Multi-stage particle windows for fast and accurate object detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.34, no. 8 , pp. 1589-1604, August 2012.   DOI   ScienceOn
8 P. Peerez, C. Hue, J. Vermaak, and M. Gangnet, "Color based probabilistic tracking," Proc. European Conference on Computer Vision, pp. 661-675, 2002.
9 S. Maji, A. Berg, and J. Malik, "Classification using intersection kernel support vector machines is efficient," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
10 B. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," Proc. DARPA Image Understanding Workshop, pp. 121-130, 1981.
11 J. Kim, D. Yeom, and Y. Joo, "Fast and robust algorithm of tracking multiple moving objects for intelligent video surveillance systems," IEEE Trans. Consumer Electronics, vol. 57, no. 3, pp. 1165-1170, August 2011.   DOI   ScienceOn
12 S. Lee, T. Kim, J. Yoo, and J. Paik, "Abnormal Behavior Detection Based on Adaptive BackgroundGeneration for Intelligent Video Analysis", Journal of The Institute of Electronics Engineers of Korea, vol. 48SP, no. 1, pp. 111-121, January 2011.