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29 April 2010 Anomaly detection in forward looking infrared imaging using one-class classifiers
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In this paper we describe a method for generating cues of possible abnormal objects present in the field of view of an infrared (IR) camera installed on a moving vehicle. The proposed method has two steps. In the first step, for each frame, we generate a set of possible points of interest using a corner detection algorithm. In the second step, the points related to the background are discarded from the point set using an one class classifier (OCC) trained on features extracted from a local neighborhood of each point. The advantage of using an OCC is that we do not need examples from the "abnormal object" class to train the classifier. Instead, OCC is trained using corner points from images known to be abnormal object free, i.e., that contain only background scenes. To further reduce the number of false alarms we use a temporal fusion procedure: a region has to be detected as "interesting" in m out of n, m<n, consecutive frames in order to be reported as abnormal. To choose the best classifier for our task, we compare the performance of three OCCs: nearest neighbor (OCNN), SVM (OC-SVM) and Gaussian mixture (OC-GM). The comparison is performed using a set of about 900 background point neighborhoods for training and 400 for testing. The best performing OCC is then used to detect abnormal objects in a set of IR video sequences obtained on a 1 mile long country road.
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Mihail Popescu, Kevin Stone, Timothy Havens, Dominic Ho, and James Keller "Anomaly detection in forward looking infrared imaging using one-class classifiers", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76642B (29 April 2010);

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