Target detection for low cost uncooled MWIR cameras based on empirical mode decomposition |
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Institution: | 1. Department of Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá, 28871 Alcalá de Henares, Spain;2. Department of Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid, 28911 Leganés, Spain;1. School of Energy and Power Engineering, Beihang University, Beijing 100191, China;2. Collaborative Innovation Center for Advanced Aero-Engine, Beijing 100191, China;1. Institut für Angewandte Physik, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 10, Tübingen 72076 Germany;2. Angewandte Mineralogie, Universität Tübingen, Wilhelmstrasse 56, D-72074 Tübingen, Germany;1. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA;2. Nanolight, Inc., Norman, OK 73069, USA |
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Abstract: | In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance. |
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Keywords: | Change detection Background subtraction Empirical Mode Decomposition (EMD) Intrinsic Mode Function (IMF) Drift Denoising |
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