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DOE OSTI · 2430331

Identifying and Explaining Anomalous Activity in Surveillance Video with Compression Algorithms

Smith, Michael Reed·Bisila, Jonathan Daniel·Gooding, Renee Lynne·Ting, Christina
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Abstract

The primary purpose of this document is to outline the progress made on the LDRD titled “Identifying and Explaining Anomalous Activity in Surveillance Video with Compression Algorithms” in FY22 and FY23. In this LDRD, we explored the usage of compression-based analytics to identify anomalous activity in video. We developed a novel algorithm, Spatio-Temporal N-Gram PPM (STNG PPM) that accounts for spatially and temporally aware anomalies in video. We extracted features using motions vectors from video as well as operating on the raw features. STNG PPM is comparable to many deep learning approaches but does not require specialized hardware (GPUs) to run efficiently. We also examine the evaluation metrics and propose novel measures addressing faults in the current evaluation measures.

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97 MATHEMATICS AND COMPUTING

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Smith, Michael Reed, Bisila, Jonathan Daniel, Gooding, Renee Lynne, Ting, Christina. 2023-09-01. Identifying and Explaining Anomalous Activity in Surveillance Video with Compression Algorithms. https://doi.org/10.2172/2430331

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