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Moving object detection is about foreground and background separation based on motion detection. Detecting moving objects from similarly colored background (known as camouflage problem) has been a long-standing open question in this field. Discriminative modeling (DM), which focuses on enhancing the performance to distinguish foreground from background with discriminative features and well-designed classifiers, has been widely used for moving object detection. However, DM may tend to fail when encountering the camouflage problem, as the class separability in camouflaged areas is generally poor. In this paper, we propose a new strategy, camouflage modeling (CM), to identify camouflaged foreground pixels. In view of the fact that camouflage involves both foreground and background, we need to model both the background and the foreground, and compare them in a well-designed way in camouflage detection. Specifically, we develop a global model for the background, and an integration of global and local models for the foreground, respectively. Based on both background and foreground models, we introduce a factor to measure the degree of camouflage, and further identify truly camouflaged areas. In view of the fact that a moving object is usually composed of both camouflaged and noncamouflaged areas, CM and DM are fused in a Bayesian framework to perform complete object detection. Experiments are conducted on testing sequences to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Abstract: On minor component analysis (MCA) neural networks, a new algorithm is proposed. It is a self-stabilizing MCA algorithm, which means that the sign of the temporal change of the weight vector length is independent of the presented input vector. Algorithms without this property may suffer fluctuations and divergence. With suitable conditions on the initial weight vector and learning rate, a rigorous global convergence proof is given. The techniques used in the proof will be useful in many research issues such as independent component analysis, principle component analysis, etc. [Copyright &y& Elsevier]