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(MCD)-C-4: A Robust Change Detection Method for Intelligent Visual Surveillance
Jul 10, 2018Author:
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 Title: (MCD)-C-4: A Robust Change Detection Method for Intelligent Visual Surveillance  

Authors: Wang, KF; Gou, C; Wang, FY  

Author Full Names: Wang, Kunfeng; Gou, Chao; Wang, Fei-Yue  

Source: IEEE ACCESS, 6 15505-15520; 10.1109/ACCESS.2018.2812880 2018  

Language: English  

Abstract: In this paper, we propose a robust change detection method for intelligent visual surveillance. This method, named (MCD)-C-4, includes three major steps. First, a sample-based background model that integrates color and texture cues is built and updated over time. Second, multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation) are extracted by comparing the input frame with the background model, and a multi-view learning strategy is designed to online estimate the probability distributions for both foreground and background. The three features are approximately conditionally independent, making multi-view learning feasible. Pixel-wise foreground posteriors are then estimated with Bayes rule. Finally, the Markov random field (MRF) optimization and heuristic postprocessing techniques are used sequentially to improve accuracy. In particular, a two-layer MRF model is constructed to represent pixel-based and superpixel-based contextual constraints compactly. Experimental results on the CDnet dataset indicate that (MCD)-C-4 is robust under complex environments and ranks among the top methods.  

ISSN: 2169-3536  

IDS Number: GB7LM  

Unique ID: WOS:000429256000001

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