M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance
This addresses the problem of accurate change detection in complex surveillance environments for security and monitoring applications, representing an incremental improvement over existing methods.
The paper tackles the problem of change detection in visual surveillance by proposing M4CD, a method that integrates color and texture cues with multi-source learning and Markov random field optimization, achieving robust performance that ranks among the top methods on the CDnet dataset.
In this paper, we propose a robust change detection method for intelligent visual surveillance. This method, named M4CD, includes three major steps. Firstly, a sample-based background model that integrates color and texture cues is built and updated over time. Secondly, 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-source learning strategy is designed to online estimate the probability distributions for both foreground and background. The three features are approximately conditionally independent, making multi-source learning feasible. Pixel-wise foreground posteriors are then estimated with Bayes rule. Finally, the Markov random field (MRF) optimization and heuristic post-processing 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 M4CD is robust under complex environments and ranks among the top methods.