Small object detection in forward-looking infrared images with sea clutter using context-driven Bayesian saliency model |
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| Institution: | 1. State Key Laboratory LIESMARS, Wuhan University, Wuhan 430079, PR China;2. Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln 68503, NE, United States;3. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, PR China;1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;3. State Key Lab. LIESMARS, Wuhan University, Wuhan 430079, China;1. State Key Lab. LIESMARS, Wuhan University, Wuhan 430079, China;2. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China;3. CNRS LTCI, Telecom ParisTech, Paris 75013, France |
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| Abstract: | There are two common challenges for small object detection in forward-looking infrared (FLIR) images with sea clutter, namely, detection ambiguity and scale variance. This paper presents a context-driven Bayesian saliency model to deal with these two issues. By inspecting the camera geometry of the FLIR imaging under the background of sea and sky, we observed that there exists dependency relationship between the locations and scales at which objects may occur, and the context which is defined to be the location of horizon line. Based on this observation, we propose to incorporate contextual information into the basic bottom-up saliency computation, and a unified Bayesian model is developed to achieve this goal. The proposed model is generic and can be potentially applied to other circumstances where context is available for facilitating object detection. Experimental results have demonstrated the effectiveness of our method. |
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| Keywords: | Forward-looking infrared image Small object detection Visual saliency Graphical model Context |
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