Improving mixture Gaussian background model by integrating trace information obtained from Kalman filter

2010 International Conference on Communications, Circuits and Systems, ICCCAS 2010 - Proceedings

Volumn , Issue , 2010, Pages 378-382

  He, Shan ^a   Guan, Qing ^a   Xu, Sheng ^a   Li, Ying ^a   Wu, Yao ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ABNORMAL BEHAVIOR; BEHAVIOR UNDERSTANDING; CERTAIN RULE; FOREGROUND EXTRACTION; GAUSSIAN BACKGROUND; ILLUMINATION CHANGES; INTELLIGENT VIDEO; MORPHOLOGICAL CHANGES; MOVING OBJECTS; TRACE INFORMATION;

ARTIFICIAL INTELLIGENCE; KALMAN FILTERS; MODELS; OBJECT RECOGNITION;

COMPUTER VISION;

EID: 78249284078     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCCAS.2010.5581986     Document Type: Conference Paper

References (13)

1. Klappstein, J., et al., Moving object segmentation using optical flow and depth information. Pr oc. PSIVT (T Wada, F. Huang, and S Lin, editor s), L NCS, 2009.54 14 pp. 6 1 r-623.
2. Song, x., et al. A Robust Moving Objects Detection Algorithm Based on Gaussian Mixture Model. in Information Technology and Computer Science, 2009. ITCS 2009. International Conference on: pp. 566-569. 2009
3. Ridder, C, O Munkelt, and H. Kirchner. "Adaptive background estimation and foreground detection using kalman1iltering". in Proc. Of International Conference on recent Advances in Mechatronics, ICRAM'95, UNESCO Chair on Mechatronics: pp. 193- 199. 1995
4. Wren, C, et al. Pfinder: real-time tracking of the human body. In Automatic Face and Gesture Recognition, 1996., Proceedings of the Second International Conference on: pp. 5 1-56. 1996
5. Stauffer, C and WEL. G rimson. Adaptive background mixture models for real-time tracking. in Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.: pp. 252 Vol. 2. 1999
6. Horprasert, T, D. Har wood, and L. D avis. A statistical approach for real-time robust background subtraction and shadow detection: pp. 256-261
7. Kalman, R., A new approach to linear filtering and prediction problems. Journal of basic Engineering, 1960. 82( 1) pp. 35-45.
8. Yaxin, G., et al. An efficient particle filter based distributed track-before-detect algorithm for weak targets. in Radar Conference, 2009 lET International: pp. 1-6.2009
9. Comaniciu, D., V R amesh, and PMeer. Real-time tracking of non-rigid objects using mean shift. in IEEE Conference on Computer Vision and Pattern Recognition, 2000. Proceedings. : pp. 142- 149 v ol.2. 2000
10. Bouwmans, T, F. EI Baf, and B. Vachon, Background Modeling using Mixture of Gaussians for Foreground Detection-A Survey. Recent Patents on Computer Science, 2008. 1(3) pp.2 19-237.
11. Yunchu, Z., et al. An adaptive mixture Gaussian background model with online background reconstruction and adjustable foreground mergence time for motion segmentation. in Industrial Technology, 2005. ICIT 2005. IEEE International Conference on: pp. 23-27. 2005
12. WANG Liang, H.F., FENG Shaotong, NIE Shouping, WANG Ming, An Improved Pixel Labeled Algorithm in Binary Image. Journal OF NANJING NORMAL UNIVERSITY ( ENG INGEERING AND TECHNOL OG Y), 2006. 6 pp.23-25 ,29.
13. Tan F ang, G.Q., XU Sheng, F eng Shi- min A method for robust recognition and tracking of multiple objects. in Communications, Circuits and Systems, 2009. ICCCAS 2009. International Conference on. Milpitas, CA pp. 464 - 468 2009