20 May 2013 Hot spot detection and classification in LWIR videos for person recognition
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Abstract
Person recognition is a key issue in visual surveillance. It is needed in many security applications such as intruder detection in military camps but also for gaining situational awareness in a variety of different safety applications. A solution for LWIR videos coming from a moving camera is presented that is based on hot spot classification to distinguish persons from background clutter and other objects. We especially consider objects in higher distance with small appearance in the image. Hot spots are detected and tracked along the videos. Various image features are extracted from the spots and different classifiers such as SVM or AdaBoost are evaluated and extended to utilize the temporal information. We demonstrate that taking advantage of this temporal context can improve the classification performance.
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Michael Teutsch, Thomas Müller, "Hot spot detection and classification in LWIR videos for person recognition", Proc. SPIE 8744, Automatic Target Recognition XXIII, 87440F (20 May 2013); doi: 10.1117/12.2015754; https://doi.org/10.1117/12.2015754
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