13 April 2009 Detecting people in IR border surveillance video using scale invariant image moments
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This paper describes a real-time system for detecting people in infrared video taken by a re-locatable camera tower suitable for border monitoring. Wind effects cause the camera to sway, so typical background modeling techniques prove difficult to apply. Instead, detection is performed using a supervised classifier over a set of seven Scale Invariant Image Moments. Blobs images are generated with a simple application of thresholding and dilation, yielding a set of possible targets. For each potential target, the Scale Invariant Moments are computed and classified as "Person" or "Non-Person." We present three methods for training the classifier: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and a two-layer Neural Network (NN). We compare the accuracy for the three methods. Results are presented for sample videos, showing acceptable accuracy while maintaining real time throughput. The key advantages of this method are real-time performance and tolerance of random ego motion.
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Stephen O'Hara, Stephen O'Hara, Amber Fischer, Amber Fischer, "Detecting people in IR border surveillance video using scale invariant image moments", Proc. SPIE 7340, Optical Pattern Recognition XX, 73400L (13 April 2009); doi: 10.1117/12.818905; https://doi.org/10.1117/12.818905


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