Video Anomaly Detection using Likelihood Statistical Texture Feature Representation in Surveillance Video
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Abstract
An anomaly detection system is challenging to develop due to the non-deterministic nature and lack of a clear definition of abnormal events. To address this issue, this paper introduces the Likelihood Statistical Texture Feature Representation (LSTFR) method, which uses CSR (Co-occurrence with Stationary occurrences Representation) to construct spatial activity patterns using gray-level co-occurrence matrices with likelihood estimation. Additionally, LSTFR constructs a composition histogram representation to model normal behavior. The occurrence rate in LSTFR is characterized by a histogram representation that depends on the spatio-temporal information of the frame sequence. To efficiently classify events using LSTFR, this paper employs Convolutional Long Short-Term Memory (conv-LSTM), where the histogram representation of LSTFR is automatically modeled through training with normal events. The efficiency of the proposed approach is evaluated using three datasets: UMN, Subway, and Avenue. Finally, the results of the proposed method are compared with several existing algorithms.
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