Gaussian-Based codebook model for video background subtraction

Authors:
Yongbin Li;Feng Chen;Wenli Xu;Youtian Du
Affiliations:
Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

Citing 2
Cited 1

Effective Gaussian Mixture Learning for Video Background Subtraction

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Real-time foreground-background segmentation using codebook model

Real-Time Imaging

Spatial codebook for robust background detection in visual information analysis

Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

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Abstract

As an effective method of background subtraction, codebook model suffers from unacceptable false negative detection rate in many situations due to its quantization criterion. In this paper, we propose an improved codebook model to solve this problem. Instead of using the original quantization criterion, we quantize the temporal series of the observations at a given pixel into codewords based on the Gaussian distribution assumption. We have performed this approach in our surveillance system for outdoor scenes and achieved excellent detection results.