Browse > Article
http://dx.doi.org/10.12672/ksis.2013.21.3.011

Background and Local Histogram-Based Object Tracking Approach  

Kim, Young Hwan (Alticast Corp.)
Park, Soon Young (Dept. of Electronics Eng., Mokpo National Uni.)
Oh, Il Whan (Dept. of Electronics Eng., Mokpo National Uni.)
Choi, Kyoung Ho (Dept. of Electronics Eng., Mokpo National Uni.)
Publication Information
Spatial Information Research / v.21, no.3, 2013 , pp. 11-19 More about this Journal
Abstract
Compared with traditional video monitoring systems that provide a video-recording function as a main service, an intelligent video monitoring system is capable of extracting/tracking objects and detecting events such as car accidents, traffic congestion, pedestrian detection, and so on. Thus, the object tracking is an essential function for various intelligent video monitoring and surveillance systems. In this paper, we propose a background and local histogram-based object tracking approach for intelligent video monitoring systems. For robust object tracking in a live situation, the result of optical flow and local histogram verification are combined with the result of background subtraction. In the proposed approach, local histogram verification allows the system to track target objects more reliably when the local histogram of LK position is not similar to the previous histogram. Experimental results are provided to show the proposed tracking algorithm is robust in object occlusion and scale change situation.
Keywords
Video Surveillance; Object Tracking; Road Monitoring; Object Tracking;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ali, A; Aggarwal, J. 2001, Segmentation and recognition of continuous human activity, Paper presented at the IEEE workshop on Detection and Recognition of Events in Video, July 28-35.
2 Bhattacharyya, A. 1943, On a measure of divergence between two statistical populations defined by probability distributions, Bulletin of the Calcutta Mathematical Society, 35:99-109.
3 Feng, J. F; Zhu, G. Y; Liu, Z. H; Li, Y. 2009, Research of Vehicle Navigation Bsed Video-GIS, Journal of Spatial Information Society, 11(2):39-44.
4 Han, J. H. 2009, Video Surveillance and Analysis Algorithms: Technologies and Trends, Journal of the Koreas Institute of Electronics Engineers, 36(10):18-29.
5 Haralick, R; Shanmugam, B; Dinstein, I. 1973, Textural features for image classification. IEEE trans. Syst. Man & Cyber., 33(3):610-622.
6 Horn, B.K.P; Schunck, B.G. 1981, Determining optical flow, Artificial Intelligence, 17:185-203.   DOI   ScienceOn
7 Kullback, S; Burnham, K. P. 1987, Letter to the Editor: The Kullback-Leibler distance, The American Statistician 41(4):340-341.
8 Levina, E; Bickel, P. 2001, The Earth Mover's Distance is the Mallows Distance: Some Insights from Statistics, Paper presented at ICCV, July 251-256.
9 Lucas, B. D; Kanade, T. 1981, An iterative image registration technique with an application to stereo vision, Paper presented at the Imaging Understanding Workshop, April 121-130.
10 Ning, J; Zhang, L; Zhang, D; Wu, C. 2012, Robust mean-shift tracking with corrected background-weighted histogram, Computer Vision, IET, 6(1):62-69.   DOI   ScienceOn
11 Oliver, N; Rosario, B; Pentland, A. 2000, A bayesian computer vision system for modeling human interactions. IEEE trans. on PAMI, 22(8):831-843.   DOI   ScienceOn
12 Paragios, N; Deriche, R. 2002, Geodesic active regions and level set methods for supervised texture segmentation, International Journal of Computer Vision 46(3):223-247.   DOI   ScienceOn
13 Stauffer, C; Grimson, W. 1999, Adaptive background mixture models for real-time tracking, Paper presented at CVPR, June 246-252.
14 Veenman, C; Reinders, M; Backer, E. 2001, Resolving motion correspondence for densely moving points, IEEE Trans. PAMI. 23(1):54-72.   DOI   ScienceOn
15 Wren, C. R; Azarbayejani, A; Drarrell, T; Pentland, A. P. 1997, Pfinder; Realtime tracking of the human body, IEEE trans. on PAMI, 19(7):780-785.   DOI   ScienceOn
16 Wu, Y; Cheng, J; Wang, J; Lu, H; Wang, J; Ling, H; Blasch, E; Bai, L. 2012, Real-Time Probabilistic Covariance Tracking With Efficient Model Update, IEEE Trans. Image Processing, 21(5):2824-2837.   DOI   ScienceOn
17 Yilmaz, A; Li, X; Shah, M. 2004, Contour based object tracking with occlusion handling in video acquired using mobile cameras, IEEE Trans. PAMI, 26(11):1531-1536.   DOI   ScienceOn
18 Yoon, C. L; Kim, H. C; Kim, K. O. 2009, 3-D GIS-based Real-time Video Visualization Technology, Journal of Spatial Information Society, 11(1):63-70.
19 Zhu, S; Yuille, A. 1996, Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation, IEEE Trans. PAMI. 18(9):884-900.   DOI   ScienceOn
20 Zivkovic, Z; Heijden, F. 2006, Efficient adaptive density estimation per image pixel for the task of background subtraction, Pattern Recognition Letters, 27(7):773-780.   DOI   ScienceOn
21 Zoidi, O; Tefas, A; Pitas, I. 2013, Visual Object Tracking Based on Local Steering Kernels and Color Histograms, IEEE Transactions on Circuits and Systems for Video Technology, 23(5):870-882.   DOI   ScienceOn