Detection of Moving Objects with Fuzzy Color Coherence Vector | Zendy

Yulong Qiao | Zendy; Kailong Yuan | Zendy; Chunyan Song | Zendy; Xuezhi Xiang | Zendy

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Detection of Moving Objects with Fuzzy Color Coherence Vector

Author(s) -

Yulong Qiao,

Kailong Yuan,

Chunyan Song,

Xuezhi Xiang

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/138065

Subject(s) - artificial intelligence , background subtraction , computer vision , pattern recognition (psychology) , color histogram , histogram , fuzzy logic , color normalization , coherence (philosophical gambling strategy) , computer science , color space , mathematics , foreground detection , pixel , color image , image processing , image (mathematics) , statistics

Background subtraction is a popular method for detecting foreground that is widely adopted as the fundamental processing for advanced applications such as tracking and surveillance. Color coherence vector (CCV) includes both the color distribution information (histogram) and the local spatial relationship information of colors. So it overcomes the weakness of the conventional color histogram for the representation of an object. In this paper, we introduce a fuzzy color coherence vector (FCCV) based background subtraction method. After applying the fuzzy c-means clustering to color coherence subvectors and color incoherence subvectors, we develop a region-based fuzzy statistical feature for each pixel based on the fuzzy membership matrices. The features are extracted from consecutive frames to build the background model and detect the moving objects. The experimental results demonstrate the effectiveness of the proposed approach

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