[1] Mao Yanfen, Shi Pengfei,. Multimodal background model with noiseand shadow suppression for moving object detection [J]. Journal of Southeast University (English Edition), 2004, 20 (4): 423-426. [doi:10.3969/j.issn.1003-7985.2004.04.006]

Copy

Multimodal background model with noiseand shadow suppression for moving object detection()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

20

Issue:

2004 4

Page:

423-426

Research Field:

Computer Science and Engineering

Publishing date:

2004-12-30

Info

Title:

Multimodal background model with noiseand shadow suppression for moving object detection

Author(s):

Mao Yanfen;  Shi Pengfei

School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China

Keywords:

video surveillance;  background model;  kernel density estimation;  shadow suppression;  hue-max-min-diff(HMMD)color space

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2004.04.006

Abstract:

A statistical multimodal background model is described for moving object detection in video surveillance. The solution to some of the problems such as illumination changes, initialization of model with moving objects, and shadows suppression is provided. The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity. Pixel’s neighbor information is considered to remove noise due to camera jitter and small motion in the scene. The hue-max-min-diff color information is used to detect and suppress moving cast shadows. The effectiveness of the proposed method in the foreground segmentation is demonstrated in the traffic surveillance application.

References:

[1] McKenna S J, Jabri S, Duric Z, et al. Tracking groups of people [J]. Computer Vision and Image Understanding, 2000, 80(1): 42-56.
[2] Rittscher J, Kato J, Joga S, et al. A probabilistic background model for tracking [A]. In: Proceedings of the 6th European Conference on Computer Vision [C]. Dublin, Ireland, 2000. 336-350.
[3] Wren C, Pentland A. Pfinder: real-time tracking of the human body [J]. IEEE Transaction on Pattern Analysis
　　and Machine Intelligence, 1997, 19(7): 780-785.
[4] Friedman N, Russell S. Image segmentation in video sequences: a probabilistic approach [A]. In: Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence [C]. San Francisco, CA: Morgan Kaufmann Publishers, 1997. 175-181.
[5] Stauffer C, Grimson W E L. Learning patterns of activity using real-time tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757.
[6] Haritaoglu I, Harwood D, Davis L S. W4: real-time surveillance of people and their activities [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 809-830.
[7] Toyama K, Krumm J, Brumitt B, et al. Wallflower: principles and practice of background maintenance [A]. In: Proceedings of International Conference on Computer Vision [C]. Kerkyra, Greece, 1999. 255-261.
[8] Elgammal A, Harwood D, Davis L S. Non-parametric model for background subtraction [A]. In: Proceedings of the 6th European Conference on Computer Vision [C]. Dublin, Ireland, 2000. 751-767.
[9] Manjunath B S, Ohm J R, Vasudevan V V, et al. Color and texture descriptors [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(6): 703-715.
[10] Prati A, Cucchiara R, Mikic I, et al. Analysis and detection of shadows in video streams: a comparative evaluation [A]. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition [C]. Kauai, Hawaii, 2001. 571-576.

Memo

Memo:

Biographies: Mao Yanfen(1975—), female, graduate; Shi Pengfei(corresponding author), male, professor, pfshi@sjtu.edu.cn.

Common functions