Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3259
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dc.contributor.authorSandula, Pavan-
dc.contributor.authorOkade, Manish-
dc.date.accessioned2019-03-12T04:15:36Z-
dc.date.available2019-03-12T04:15:36Z-
dc.date.issued2019-02-
dc.identifier.citation25th National Conference on Communications (NCC 2019), Bangalore, India, 20-23 February 2019.en_US
dc.identifier.urihttp://hdl.handle.net/2080/3259-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractThis paper presents a novel compressed domain technique for detecting zooming camera in video sequences and its further classification into zoom-in camera and zoomout camera. The inter-frame block motion vector field serves as the input to the proposed system which is partitioned into four representative quadrants for analysis purposes.The histograms of these four quadrants are analyzed utilizing histogram intersection feature for zoom motion detection while the cumulative histogram of these four quadrants are analyzed utilizing Kullback-Leibler divergence feature for zoom motion classification purposes. Experimental validation carried out utilizing block motion vectors extracted using Exhaustive Search Motion Estimation algorithm as well as H.264 decoded block motion vectors demonstrate superior performance in comparison to existing techniques.en_US
dc.subjectZoom motionen_US
dc.subjectHistogram intersectionen_US
dc.subjectKullbackLeibler divergenceen_US
dc.subjectCamera motionen_US
dc.subjectCompressed domainen_US
dc.subjectSupport vector machineen_US
dc.subjectBlock motion vectorsen_US
dc.titleCamera Zoom Detection and Classification Based on Application of Histogram Intersection and Kullback Leibler Divergenceen_US
dc.typeArticleen_US
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