Background reconstruction algorithm based on pixel intensity classification

Ruan Jian Xue Bao/Journal of Software

Volumn 16, Issue 9, 2005, Pages 1568-1576

  Hou, Zhi Qiang ^a   Han, Chong Zhao ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Background reconstruction; Background subtraction; Image segmentation; Motion detection; Tracking; Visual surveillance

Indexed keywords

ADAPTIVE ALGORITHMS; IMAGE SEGMENTATION; MOTION PICTURES; SURVEILLANCE RADAR; TRACKING (POSITION);

BACKGROUND RECONSTRUCTION; BACKGROUND SUBTRACTION; MOTION DETECTION; TRACKING; VISUAL SURVEILLANCE;

OBJECT RECOGNITION;

EID: 27244433269     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.1360/jos161568     Document Type: Article

References (25)

1. Horn BK, Schunk BG. Determining optical flow. Artificial Intelligence, 1981,17(1-3):185-203.
2. Smith SM, Brady JM. ASSET-2: Real-Time motion segmentation and shape tracking. IEEE Trans. on PAMI, 1995,17(8):814-820.
3. Neri A, Colonnese S, Russo G, Talone P. Automatic moving object and background separation. Signal Processing, 1998,66(2):219-232.
4. Meier T, Ngan KN. Automatic segmentation of moving objects for video object plane generation. IEEE Trans. on Circuits and Systems for Video Technology, 1998,8(5):525-538.
5. Jolly MPD, Lakshmanan S, Jain AK. Vehicle segmentation and classification using deformable templates. IEEE Trans. on PAMI, 1996,18(3):293-308.
6. Ridder C, Munkelt O, Kirchner H. Adaptive background estimation and foreground detection using Kalman-filter. In: Proc. of the Int'l Conf. on Recent Advances in Mechatronics, ICRAM'95. UNESCO Chair on Mechatronics, 1995. 193-199.
7. Friedman N, Russell S. Image segmentation in video sequences: A probabilistic approach. In: Proc. of the 13th Conf. on Uncertainty in Artificial Intelligence (UAI). San Francisco, 1997.
8. Stauffer C, Grimson WEL. Adaptive background mixture models for real-time tracking. In: Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Vol 2. 1999. 246-252.
9. KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection. In: The 2nd European Workshop on Advanced Video-based Surveillance Systems. Kingston upon Thames, 2001.
10. Elgammal A, Harwood D, Davis L. Non-Parametric model for background subtraction. In: Proc. of the 6th European Conf. on Computer Vision. Dublin Ireland, 2000.
11. Elgammal A, Duraiswami R, Harwood D, Davis L. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. of the IEEE, 2002,90(7):1151-1163.
12. Magee D. Tracking multiple vehicle using foreground, background and motion models. Image and Vision Computing, 2004,22(2):143-155.
13. Wren CR, Azarbayejani A, Darrell T, Pentland AP. Pfinder: Real-Time tracking of the human body. IEEE Trans. on PAMI, 1997,19(7):780-785.
14. Toyama K, Krumm J, Brumitt B, Meyers B. Wallflower: Principles and practice of background maintenance. In: Int'l Conf. on Computer Vision. Kerkyra Greece, 1999. 255-261.
15. Long W, Yang Y. Stationary background generation: An alternative to the difference of two images. Pattern Recognition, 1990,23(12):1351-1359.
16. Gutchess D, Trajkonic M, Cohen-Solal E, Lyons D, Jain AK. A background model initialization algorithm for video surveillance. In: Proc. of the 8th IEEE Int'l Conf. on Computer Vision. Vancouver, 2001. 733-740.
17. Gloyer B, Aghajan HK, Siu KY, Kailath T. Video-Based freeway monitoring system using recursive vehicle tracking. In: Proc. of the IS&T-SPIE Symp. on Electronic Imaging: Image and Video Processing, Vol 2421. 1995. 173-180.
18. Haritaoglu I, Harwood D, Davis L. W4: Real-Time surveillance of people and their activities. IEEE Trans. on PAMI, 2000,22(8):809-830.
19. Kornprobst P, Deriche R, Aubert G. Image sequence analysis via partial difference equations. Journal of Mathematical Imaging and Vision, 1999,11(1):5-26.
20. Zhang WT, Li XF, Li ZM. Motion analysis under high speed dense visual-target scenes. Acta Elctronica Sinica, 2000,28(10):114-117 (in Chinese with English abstract).
21. Yu SS, Xiao DG, Zhou JL, Jiang G. Adaptive background subtraction algorithm. Chinese Journal of Mini-Micro Systems, 2003, 24(7):1331-1334 (in Chinese with English abstract).
22. Cen F, Qi FH, Chen ML. Multi-Distribution model for background subtraction in long term video surveillance system. Chinese Journal of Infrared and Millimeter Waves, 2002,21(1):59-63 (in Chinese with English abstract).
23. Herrero E, Orrite C, Senar J. Detected motion classification with a double-background and a neighborhood-based difference. Pattern Recognition Letters, 2003,24(12):2079-2092.
24. Eng HL, Toh KA, Kam AH, Wang J, Yau WY. An automatic drowning detection surveillance system for challenging outdoor pool environments. In: Proc. the 9th IEEE Int'l Conf. on Computer Vision. 2003. 532-539.
25. Mittal A, Paragios N. Motion-Based background subtraction using adaptive kernel density estimation. In: Proc. of the 2004 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Vol 2. 2004. 302-309.