Background modeling method based on sequential kernel density approximation

Proceedings of the 2008 Chinese Conference on Pattern Recognition, CCPR 2008

Volumn , Issue , 2008, Pages 187-192

  Wang, Huan ^a   Ren, Ming Wu ^a   Yang, Jing Yu ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Background modeling; Kernel density approximation; Moving object detection

Indexed keywords

PATTERN RECOGNITION; POLYNOMIAL APPROXIMATION; REAL TIME SYSTEMS; VIDEO RECORDING;

BACKGROUND MODELING; BACKGROUND MODELS; BACKGROUND SUBTRACTION METHODS; BACKGROUND SUBTRACTIONS; KERNEL DENSITY APPROXIMATION; MOVING OBJECT DETECTION; REAL VIDEO SEQUENCES;

OBJECT RECOGNITION;

EID: 58049114533     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CCPR.2008.44     Document Type: Conference Paper

References (13)

1. A.Talukder, L.Matthies. Real-time detection of moving object from moving vehicles using dense stereo and optical flow. IEEE Conference on Intelligent Robots and Systems, 2004.
2. L.Wixson. Detecting Sailient Motion by Accumulating Directionally Consistent flow. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(8):774-780, 2000.
3. R.T.Collins, A.J.Lipton, T.Kanade, et al., A system for video surveillance and monitoring. Technical Report, Carnegie Mellon University 2000.
4. R.Cucchiara, M. Piccardi, Vehicle detection under day and night illumination. Proceedings of the Third International Symposia on intelligent industrial Automation and Soft Computing, 1999.
5. T.Horprasert, D.Harwood, L.S.Davis, A statistical approach for real-time robust background subtraction and shadow detection. Proc. of IEEE ICCV Frame-rate workshop, 1999, pp.1-19.
6. C.R.Wren, A. Azarbayejani, T.Darrell, et.al. Pfinder: real-time tracking of the human body. IEEE Tran. On Pattern Analysis and machine intelligence, 19(7):780-785,1997.
7. C.Stauffer, W.E.L. Grimson. Adaptive background mixture models for real-time tracking. Proc. of IEEE conference on computer vision and pattern recognition, 1999, pp.246-252.
8. A. Elgammal, D. Harwood, L.S.Davis. Non-parametric model for background subtraction. Proc. of European Conference on computer vision, 2000, pp. 751-767.
9. B.Stenger, V.Ramesh, N.Paragios, et.al. Topology free hidden Markov models: application to background modeling. Proc. of IEEE International conference on Computer Vision, 2001, pp.249-301.
10. C.Ridder, O.Munkelt, H.Kirchner. Adaptive background estimation and foreground detection using kalman filtering. Proc. of International Conference on Recent Advances in Mechatronics, 1995, pp.193-199.
11. J.Zhong, S.Sclaroff, Segmenting foreground objects from a dynamic textured background via a robust kalman filter, Proc. of International Conference on Computer vision, 2003, pp.44-50.
12. K.Kim, T.H.Chalidabhongse, D.Harwood. Real-time foreground-background segmentation using codebook model. Real-time Imaging, 11(3):167-256, 2005.
13. H. Bohyung, D. Comanuciu, Z. Ying. D. Larry S, Sequential kernel density approximation and its application to real-time visual tracking. IEEE Trans. on Pattern recognition and machine intelligence,30(7), 2008, pp. 1186-1197.