Multi-feature fusion for surveillance video indexing
Ebroul Izquierdo, Virginia Fernández Arguedas, Qianni Zhang, Krishna Chandramouli · Research Repository (Delft University of Technology) · 2011
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.