Moving object detection based on Gaussian mixture model within the quotient space hierarchical theory

Authors:
Yanping Zhang;Yunqiu Bai;Shu Zhao
Affiliations:
Key Lab of Ministry of Education for CI & SP, Anhui University, Hefei, Anhui, China;Key Lab of Ministry of Education for CI & SP, Anhui University, Hefei, Anhui, China;Key Lab of Ministry of Education for CI & SP, Anhui University, Hefei, Anhui, China
Venue:
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Year:
2010

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Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

Based on the deficiencies of the Gaussian mixture model (GMM), the improvement is proposed in this paper. The image of video is partitioned into coarse Granularities by equivalence relation R, and the Quotient space can be obtained. Then the moving object is detected within it. The experiments show that the algorithm can improve the detection rate of the moving object without influencing to identify the object.