A Bayesian Approach to Camouflaged Moving Object Detection

• Published in: IEEE transactions on circuits and systems for video technology Vol. 27; no. 9; pp. 2001 - 2013
• Main Authors: Zhang, Xiang, Zhu, Ce, Wang, Shuai, Liu, Yipeng, Ye, Mao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Background subtraction; Bayes methods; Bayesian analysis; Camouflage; camouflage problem; Color; Computational modeling; Feature extraction; Hidden Markov models; Modelling; Motion perception; moving object detection; Moving object recognition; Object detection; video surveillance
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2016.2555719

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.