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.
CITATION STYLE
Rao, T. J. N., Girish, G. N., & Rajan, J. (2017). An improved contextual information based approach for anomaly detection via adaptive inference for surveillance application. In Advances in Intelligent Systems and Computing (Vol. 459 AISC, pp. 133–147). Springer Verlag. https://doi.org/10.1007/978-981-10-2104-6_13
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