Abstract
This work introduces a novel motion descriptor that enables human activity classification in video-surveillance applications. The method starts by computing a dense optical flow, providing instantaneous velocity information for every pixel. The obtained flow is then characterized by a per-frameorientation histogram, weighted by the norm, with orientations quantized to 32 principal directions. Finally, a set of global characteristics is determined from the temporal series obtained from each histogram bin, forming a descriptor vector. The method was evaluated using a 192-dimensional descriptor with the classical Weizmann action dataset, obtaining an average accuracy of 95%. For more complex surveillance scenarios, the method was assessed with the VISOR dataset, achieving a 96.7% of accuracy in a classification task performed using a Support Vector Machine (SVM) classifier.
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Martínez, F., Manzanera, A., Romero, E. (2012). A Motion Descriptor Based on Statistics of Optical Flow Orientations for Action Classification in Video-Surveillance. In: Wang, F.L., Lei, J., Lau, R.W.H., Zhang, J. (eds) Multimedia and Signal Processing. CMSP 2012. Communications in Computer and Information Science, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35286-7_34
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DOI: https://doi.org/10.1007/978-3-642-35286-7_34
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