A study of deep convolutional auto-encoders for anomaly detection in videos

•Deep convolutional auto-encoder for anomaly detection in videos.•Fusion of low-level (frames) with high-level (appearance and motion features) information.•Study of the influence of video complexity in the classification performance.•Use of reconstruction errors from convolutional auto-encoder as a...

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Bibliographic Details
Published in:Pattern recognition letters 2018-04, Vol.105, p.13-22
Main Authors: Ribeiro, Manassés, Lazzaretti, André Eugênio, Lopes, Heitor Silvério
Format: Article
Language:English
Subjects:
Anomalies
Anomaly detection
Artificial neural networks
Behavior
Coders
Computer vision
Convolutional auto-encoder
Deep learning
Error detection
Feature extraction
Image reconstruction
Neural networks
Object recognition
Pattern recognition
Surveillance
Temporal variations
Video
Video data
Vision systems
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Summary:•Deep convolutional auto-encoder for anomaly detection in videos.•Fusion of low-level (frames) with high-level (appearance and motion features) information.•Study of the influence of video complexity in the classification performance.•Use of reconstruction errors from convolutional auto-encoder as anomaly scores.•Case studies with real-world video clips. The detection of anomalous behaviors in automated video surveillance is a recurrent topic in recent computer vision research. Depending on the application field, anomalies can present different characteristics and challenges. Convolutional Neural Networks have achieved the state-of-the-art performance for object recognition in recent years, since they learn features automatically during the training process. From the anomaly detection perspective, the Convolutional Autoencoder (CAE) is an interesting choice, since it captures the 2D structure in image sequences during the learning process. This work uses a CAE in the anomaly detection context, by applying the reconstruction error of each frame as an anomaly score. By exploring the CAE architecture, we also propose a method for aggregating high-level spatial and temporal features with the input frames and investigate how they affect the CAE performance. An easy-to-use measure of video spatial complexity was devised and correlated with the classification performance of the CAE. The proposed methods were evaluated by means of several experiments with public-domain datasets. The promising results support further research in this area.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2017.07.016