Browse > Article
http://dx.doi.org/10.15701/kcgs.2015.21.4.11

Multi-mode Kernel Weight-based Object Tracking  

Kim, Eun-Sub (Korea Electronics Technology Institute)
Kim, Yong-Goo (Department of Newmedia, Korean German Institute of Technology)
Choi, Yoo-Joo (Department of Newmedia, Korean German Institute of Technology)
Publication Information
Journal of the Korea Computer Graphics Society / v.21, no.4, 2015 , pp. 11-17 More about this Journal
Abstract
As the needs of real-time visual object tracking are increasing in various kinds of application fields such as surveillance, entertainment, etc., kernel-based mean-shift tracking has received more interests. One of major issues in kernel-based mean-shift tracking is to be robust under partial or full occlusion status. This paper presents a real-time mean-shift tracking which is robust in partial occlusion by applying multi-mode local kernel weight. In the proposed method, a kernel is divided into multiple sub-kernels and each sub-kernel has a kernel weight to be determined according to the location of the sub-kernel. The experimental results show that the proposed method is more stable than the previous methods with multi-mode kernels in partial occlusion circumstance.
Keywords
object tracking; mean-shift tracking; kernel-based tracking; occlusion handling;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Yilmaz, O. Javed, M. Shah. Object tracking: a survey, ACM Computing Surveys, 2006:38(4): 45, Article 13.
2 M. J. Patel, B. Bhatt. A comparative study of object tracking technoques, International Journal of Innovative Research in Science, Engineering and Technolgy, 2015: 4(3): 1361-1364.
3 D. Comaniciu, V. Ramesh, P. Meer. Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 2003:25(5):564-577.   DOI
4 Z. H. Khan, I. Y.-H. Gu, A. G. Backhouse. Robust Visual Object Tracking using Multi-Mode Anisotropic Mean Shift and Particle Filters, IEEE Transactions on Circuits and Systems for Video Technology 2011:21(1):74-87.   DOI
5 S. Zhang, Robust visual tracking based on occlusion detection and particle redistribution, ICIMCS 2010:159-162.
6 J. Jeyakar, R. V. Babu, K.R. Ramakrishnan, "Robust object tracking with background-weighted local kernels", Computer-Vision and Image Understanding 2008:112:296-309.
7 S. Zhang, H. Yao, S. Liu. Partial occlusion robust object tracking using an effective appearance model, Visual Communications and Image Processing 2010:1-8.
8 A. Adam, E. Rivlin, I. Shimshoni. Robust fragments-based tracking using the integral histogram, Proc. CVPR, 2006:1:798-805.
9 Z. Fan, Y. Wu, M. Yang. Multiple collaborative kernel tracking, Proc. CVPR, 2005:2:502-509.
10 V. Rowghanian, K. Ansari-Asl. Object tracking by mean shift and radial basis function neural networks, 2015:1-18.
11 D. Jia, L. Zhang, C. Li. The improvement of mean-shift algorithm in target tracking, International Journal of Security and Its Applications, 2015:9(2):21-28.   DOI
12 J. Ning, L. Zhang, D. Zhang, C. Wu. Robust mean-shift tracking with corrected background-weighted histogram. IET Comput. Vis, 2012:6:62-69.   DOI