A traffic video background extraction algorithm based on image content sensitivity

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6146 LNCS, Issue PART 2, 2010, Pages 603-610

  Qin, Bo ^a   Wang, Jingjing ^a   Gao, Jian ^b   Pang, Titi ^a   Su, Fang ^a  

^a OCEAN UNIVERSITY OF CHINA   (China)

^b QINGDAO UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

EE; GMM; Image Content Sensitivity; Moving region; Weighted average

Indexed keywords

BACKGROUND EXTRACTION; BACKGROUND IMAGE; CURRENT IMAGE; GAUSSIAN BACKGROUND; IMAGE CONTENT; MIXTURE GAUSSIAN MODEL; MOVING REGIONS; NUMBER OF VEHICLES; PROCESSING EFFECTS; TRAFFIC VIDEOS; WEIGHT FACTOR; WEIGHTED AVERAGES;

AUTOMOBILE EXHIBITIONS; CELLULAR AUTOMATA; IMAGE SEGMENTATION; PIXELS;

ALGORITHMS;

EID: 77954637760     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13498-2_79     Document Type: Conference Paper

References (11)

1. Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE 90(7), 1151-1162 (2002)
2. Suo, P., Wang, Y.: An improved adaptive background modeling algorithm based on Gaussian Mixture Model. In: 9th International Conference on Signal Processing, ICSP 2008, October 26-29, pp.1436-1439 (2008)
3. Turdu, D, Erdogan, H.: Improving Gaussian Mixture Model based Adaptive Background Modeling using Hysteresis Thresholding. In: IEEE 15th Signal Processing and Communications Applications, SIU 2007, pp. 1-4 (2007)
4. Kahl, F., Hartley, R., Hilsenstein, V.: Novelty detection in image sequences with dynamic background. In: Comaniciu, D., Mester, R., Kanatani, K., Suter, D. (eds.) SMVP 2004. LNCS, vol. 3247, pp. 117-128. Springer, Heidelberg (2004)
5. Li, L., Gu, I.Y.-H., Leung, M.K.H.: Adaptive background subtraction based on feedback from fuzzy classification. Optical Engineering 43(10), 2381-2394 (2004)
6. Wang, F., Dai, S.: Adaptive background update based on mixture models of Gaussian. In: 2009 IEEE International Conference on Information and Automation, ICIA 2009, pp. 336-339 (2009)
7. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252 (1999)
8. Qin, B., Ma, Z., Fang, Z., Wang, S.: Fast Detection of Vehicles Based-on the Moving Region. In: 10th IEEE International Conference on Computer-Aided Design and Computer Graphics, pp. 202-207 (2007)
9. Neuhoff, D.L., Reyes, M.G.: Entropy bounds for a Markov random subfield. In: Proceedings of IEEE International Symposium on Information Theory, pp. 309-313 (2009)
10. Hou, Z., Han, C.: A background reconstruction algorithm based on pixel intensity classification in remote video surveillance system. In: Proc. of the Seventh International Conference on Information Fusion, pp. 754-759 (2004)
11. Zhang, Y., Liang, Z., Hou, Z., Wang, H., Tan, M.: An Adaptive Mixture Gaussian Background Model with Online Background Reconstruction and Adjustable Foreground Mergence Time for Motion Segmentation. In: Proceedings of the IEEE International Conference on Industrial Technology, pp. 23-27 (2005)