Indirect target detection method in FLIR image sequences |
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| Institution: | 1. Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China;2. College of Electrical & Electronic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China;1. Advanced Communication Engineering Centre (ACE), School of Computer and Communication Engineering, Universiti Malaysia Perlis, Kangar, 01000, Perlis, Malaysia;2. Faculty of Mechanical Engineering, Universiti Malaysia Pahang, Pekan, 26600, Malaysia;3. School of Materials Engineering, Universiti Malaysia Perlis, Kangar, 02600, Perlis, Malaysia;4. Nanotechnology Research Lab, Department of Physics, Kongunadu Arts and Science College, G-N Mills, Coimbatore, 641 029, Tamil Nadu, India;5. Electronics and Communication Engineering, Christ the King Engineering College, Coimbatore, 641 104, Tamil Nadu, India;1. COREMED – Cooperative Centre for Regenerative Medicine, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria;2. Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, Graz, Austria;1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China;2. School of Data Science, Fudan University, Shanghai, China |
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| Abstract: | Due to the complexity of the scene, target detection in forward-looking infrared (FLIR) imagery is a challenging problem, especially for occluded target. The main contribution of this paper is to propose an indirect detection method for improving the recognition probability and effectiveness of target detection method in FLIR image sequences under complex conditions. The proposed method mainly includes four steps: preparation of forward-looking reference image of landmark, extraction of the real-time scene image, template matching and target location, in which some key technologies are proposed, such as perspective transformation used to solve projective problems, position prediction for improving real-time performance, and target location used for identifying the target’s position. Experimental results are shown to demonstrate the robustness and efficiency of proposed method in FLIR image sequences. |
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| Keywords: | Target detection Perspective transformation Position prediction Target location |
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