In this paper, we propose a robust change detection method for intelligent visual surveillance. This method, named M 4 CD, 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 M 4 CD is robust under complex environments and ranks among the top methods. Change detection, multimodal background, multi-view learning, conditional independence, Markov random field. VOLUME 6, 2018 2169-3536 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 15505 K. Wang et al.: M 4 CD: Robust Change Detection Method for Intelligent Visual Surveillance FIGURE 1. Flowchart of M 4 CD. The computation modules of three major steps (corresponding to Sections III-V) are shown in blue, green, and orange respectively.
Wang et al. REF proposed a M4CD model, in which builds a sample-based background model that integrates color and texture for each pixel.
$M^{4}CD$ : A Robust Change Detection Method for Intelligent Visual Surveillance
IEEE Access
IEEE Access
Computer Science