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Multi-feature fusion for surveillance video indexing

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Author: Fernandez Arguedas, V. · Zhang, Q. · Chandramouli, K. · Izquierdo, E.
Type:Conference paper
Date:2011-04-13
Publisher/Organization: TU Delft; EWI; MM; PRB
Source:WIAMIS 2011: 12th International Workshop on Image Analysis for Multimedia Interactive Services, Delft, The Netherlands, April 13-15, 2011
ISBN: 978-94-90818-00-5
Rights: (c) 2011 Fernandez Arguedas, V.; Zhang, Q.; Chandramouli, K.; Izquierdo, E.

Abstract

In this paper, we present a part of surveillance centric indexing framework aimed at studying the performance of multi-feature fusion technique for indexing objects from surveillance videos. The multi-feature fusion algorithm determines an optimal metric for fusing low-level descriptors extracted from different feature space. These low-level descriptors exhibit a non-linear behaviour and typically consist of different similarity metrics. The framework also includes a motion analysis component for the extraction of objects as blobs from individual frames. The proposed framework, in particular the multi-feature fusion algorithm is evaluated against kernel machines for indexing objects such as car and person on AVSS 2007 surveillance dataset.

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