NDLI: Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

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Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Content Provider	ACM Digital Library
Author	Denman, Simon Xu, Jingxin Rana, Rajib Fookes, Clinton Sridharan, Sridha
Abstract	Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparsecoefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.
Starting Page	25
Ending Page	30
Page Count	6
File Format	PDF
ISBN	9781450309967
DOI	10.1145/2072508.2072515
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-11-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dynamic texture Sparse coding Anomaly detection Compressed sensing Dantzig selector
Content Type	Text
Resource Type	Article

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