Dynamic background subtraction based on local dependency histogram

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 23, Issue 7, 2009, Pages 1397-1419

  Zhang, Shengping ^a   Yao, Honggxun ^a   Liu, Shaohui ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Adaptive thresholding; Background subtraction; Dynamic background; Moving object detection; Spatial dependencies

Indexed keywords

ADAPTIVE THRESHOLDING; BACKGROUND SUBTRACTION; BACKGROUND SUBTRACTION METHOD; CURRENT FRAME; DESCRIPTORS; DYNAMIC BACKGROUND; DYNAMIC SCENES; EFFECTIVE DYNAMICS; FIXED THRESHOLD; ILLUMINATION CHANGES; MOVING OBJECTS; MOVING-OBJECT DETECTION; PIXEL MATCHES; SPATIAL CONTEXT; TREE BRANCHES;

FLUORINE CONTAINING POLYMERS; JITTER; MULTIMEDIA SERVICES; PIXELS; TRACKING (POSITION);

OBJECT RECOGNITION;

EID: 70450176219     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001409007569     Document Type: Article

References (27)

1. C. J. Chang, J. W. Hsieh and Y. S. Chen, Tracking multiple moving objects using a level-set method, Int. J. Patt. Recogn. Artif. Intell. 18(2) (2004) 101-125.
2. A. Elgammal, R. Duraiswami, D. Harwood and L. S. Davis, Background and foreground modeling using nonparametric kernel density estimation for visual surveillance, Proc. IEEE 90(7) (2002) 1151-1163.
3. S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Trans. Patt. Anal. Mach. Intell. 6(6) (1984) 721-741.
4. M. Heikkila andM. Pietikainen, A texture-based method for modeling the background and detecting moving objects, IEEE Trans. Patt. Anal. Mach. Intell. 28(4) (2006) 657-662.
5. J. S. Hu and T. M. Su, Robust background subtraction with shadow and highlight removal for indoor surveillance, Proc. Int. Conf. Intelligent Robots and Systems (2006), pp. 4545-4550.
6. J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu and R. Zabih, Image indexing using color correlograms, Proc. Int. Conf. Computer Vision and Pattern Recognition (1997), pp. 762-768.
7. P. Kaew TraKulPong and R. Bowden, An improved adaptive background mixture model for real-time tracking with shadow detection, Proc. European Workshop Advanced Video Based Surveillance Systems (2001).
8. K. P. Karmann and A. Brandt, Moving object recognition using an adaptive background memory, ed. Cappellini, V., Time-Varying Image Processing and Moving Object Recognition (1990).
9. K. Kim, T. H. Chalidabhongse, D. Harwood and L. Davis, Background modeling and subtraction by codebook construction, Proc. Int. Conf. Image Processing (2004), pp. 3061-3064.
10. D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao and S. Russell, Towards robust automatic traffic scene analysis in real-time, Proc. Int. Conf. Pattern Recognition (1994), pp. 126-131.
11. H. Lee, J. Kim and H. Ko, Prediction based occluded multitarget tracking using spatio-temporal attention, Int. J. Patt. Recogn. Artif. Intell. 20(6) (2006) 925-938.
12. K. K. Lee and Y. Xu, Analysis of human walking trajectories for surveillance, Int. J. Patt. Recogn. Artif. Intell. 1(2) (2004) 169-189.
13. S. Li, Markov Random Field Modeling in Computer Vision (Springer, Berlin, 1995).
14. M. Mason and Z. Duric, Using histogram to detect and tracking objects in color video, Proc. Applied Imagery Pattern Recognition Workshop (2001), pp. 154-159.
15. C. Ridder, O. Munkelt and H. Kirchner, Adaptive background estimation and foreground detection using Kalman-filtering, Proc. Int. Conf. Recent Advances in Mechatronics (1995), pp. 193-199.
16. J. Rittscher, J. Kato, S. Joga and A. Blake, A probabilistic background model for tracking, Proc. European. Conf. Computer Vision (2000), pp. 336-350.
17. Y. Sheikh and M. Shah, Bayesian modeling of dynamic scenes for object detection, IEEE Trans. Patt. Anal. Mach. Intell. 27(11) (2005) 1778-1792.
18. C. Stauffer and W. Grimson, Learning patterns of activity using real-time tracking, IEEE Trans. Patt. Anal. Mach. Intell. 22(8) (2000) 747-757.
19. B. Stenger, V. Ramesh, N. Paragios, F. Coetzee and J. M. Buhmann, Topology free hidden Markov models: Application to background modeling, Proc. Int. Conf. Computer Vision (2001), pp. 294-301.
20. V. Takala and M. Pietikainen, Multi-object tracking using color, texture and motion, Proc. Int. Conf. Computer Vision and Pattern Recognition (2007), pp. 1-7.
21. K. Toyama, J. Krumm, B. Brumitt and B. Meyers, Wallflower: principles and practice of background maintenance, Proc. Int. Conf. Computer Vision (1999), pp. 255-261.
22. C. R. Wren, A. Azarbayejani, T. Darrell and A. P. Pentland, Pfinder: Real-time tracking of the human body, IEEE Trans. Patt. Anal. Mach. Intell. 19(7) (1997) 780-785.
23. J. Yao and J. M. Odobez, Multi-layer background subtraction based on color and texture, Proc. Int. Conf. Computer Vision and Pattern Recognition (2007), pp. 1-8.
24. W. Zajdeland and B. J. A. Kröse, A sequential Bayesian algorithm for surveillance with nonoverlapping cameras, Int. J. Patt. Recogn. Artif. Intell. 19(8) (2005) 977-996.
25. W. Zhang, X. Z. Fang, X. K. Yang and Q. M. J.Wu, Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes, J. Electron. Imag. 16(2) (2007) 023013-1-6.
26. Q. Zhang and R. Klette, Robust background subtraction and maintenance, Proc. Int. Conf. Pattern Recognition (2004), pp. 94-193
27. Z. Zivkovic, Improved adaptive Gaussian mixture model for background subtraction, Proc. Int. Conf. Pattern Recognition (2004), pp. 28-31.