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Title: An improved contextual information based approach for anomaly detection via adaptive inference for surveillance application
Authors: Rao, T.J.N.
Girish, G.N.
Rajan, J.
Issue Date: 2017
Citation: Advances in Intelligent Systems and Computing, 2017, Vol.459 AISC, , pp.133-147
Abstract: Anomalous event detection is the foremost objective of a visual surveillance system. Using contextual information and probabilistic inference mechanisms is a recent trend in this direction. The proposed method is an improved version of the Spatio-Temporal Compositions (STC) concept, introduced earlier. Specific modifications are applied to STC method to reduce time complexity and improve the performance. The non-overlapping volume and ensemble formation employed reduce the iterations in codebook construction and probabilistic modeling steps. A simpler procedure for codebook construction has been proposed. A non-parametric probabilistic model and adaptive inference mechanisms to avoid the use of a single experimental threshold value are the other contributions. An additional feature such as event-driven high-resolution localization of unusual events is incorporated to aid in surveillance application. The proposed method produced promising results when compared to STC and other state-of-the-art approaches when experimented on seven standard datasets with simple/complex actions, in non-crowded/crowded environments. � Springer Science+Business Media Singapore 2017.
Appears in Collections:2. Conference Papers

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