6 July 2017 Detecting anomalies in crowded scenes via locality-constrained affine subspace coding
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Abstract
Video anomaly event detection is the process of finding an abnormal event deviation compared with the majority of normal or usual events. The main challenges are the high structure redundancy and the dynamic changes in the scenes that are in surveillance videos. To address these problems, we present a framework for anomaly detection and localization in videos that is based on locality-constrained affine subspace coding (LASC) and a model updating procedure. In our algorithm, LASC attempts to reconstruct the test sample by its top-k nearest subspaces, which are obtained by segmenting the normal samples space using a clustering method. A sample with a large reconstruction cost is detected as abnormal by setting a threshold. To adapt to the scene changes over time, a model updating strategy is proposed. We experiment on two public datasets: the UCSD dataset and the Avenue dataset. The results demonstrate that our method achieves competitive performance at a 700 fps on a single desktop PC.
© 2017 SPIE and IS&T
Yaxiang Fan, Yaxiang Fan, Gongjian Wen, Gongjian Wen, Shaohua Qiu, Shaohua Qiu, Deren Li, Deren Li, } "Detecting anomalies in crowded scenes via locality-constrained affine subspace coding," Journal of Electronic Imaging 26(4), 043002 (6 July 2017). https://doi.org/10.1117/1.JEI.26.4.043002 . Submission: Received: 11 February 2017; Accepted: 15 June 2017
Received: 11 February 2017; Accepted: 15 June 2017; Published: 6 July 2017
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