Anomaly Detection in Videos Using Two-Stream Autoencoder with Post Hoc Interpretability

The growing interest in deep learning approaches to video surveillance raises concerns about the accuracy and efficiency of neural networks. However, fast and reliable detection of abnormal events is still a challenging work. Here, we introduce a two-stream approach that offers an autoencoder-based...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2021, Vol.2021 (1), p.7367870-7367870
Main Authors: Feng, Jiangfan, Liang, Yukun, Li, Lin
Format: Article
Language:English
Subjects:
Anomalies
Decision making
Deep learning
Distance learning
Feature maps
Methods
Neural networks
Semantics
Surveillance
Visualization
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Summary:The growing interest in deep learning approaches to video surveillance raises concerns about the accuracy and efficiency of neural networks. However, fast and reliable detection of abnormal events is still a challenging work. Here, we introduce a two-stream approach that offers an autoencoder-based structure for fast and efficient detection to facilitate anomaly detection from surveillance video without labeled abnormal events. Furthermore, we present post hoc interpretability of feature map visualization to show the process of feature learning, revealing uncertain and ambiguous decision boundaries in the video sequence. Experimental results on Avenue, UCSD Ped2, and Subway datasets show that our method can detect abnormal events well and explain the internal logic of the model at the object level.
ISSN:1687-5265
1687-5273
DOI:10.1155/2021/7367870