Unsupervised anomalous event detection in videos using spatio-temporal inter-fused autoencoder

Automatic detection, localization and interpretation of an unusual event in a sequence of video is a challenging task due to its equivocal and complex nature. The development of deep neural networks have paved the way for more efficient recognition and analysis of anomalous events in video data. Wit...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 81; no. 29; pp. 42457 - 42482
Main Authors: Aslam, Nazia, Kolekar, Maheshkumar H
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2022
Springer Nature B.V
Subjects:
1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System
Artificial neural networks
Computer architecture
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Feature extraction
Multimedia Information Systems
Neural networks
Special Purpose and Application-Based Systems
Video data
Video anomaly detection
Spatio-temporal analysis
Inter-fused autoencoder
Unsupervised learning
ISSN:1380-7501, 1573-7721
Online Access:Get full text
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Summary:Automatic detection, localization and interpretation of an unusual event in a sequence of video is a challenging task due to its equivocal and complex nature. The development of deep neural networks have paved the way for more efficient recognition and analysis of anomalous events in video data. With the introduction of convolutional neural network (CNN) and Long short-term memory (LSTM), the spatial and temporal features extraction became easier. In this paper, we propose an end-to-end trainable Inter-fused Autoencoder (IFA) which is designed using the assemblage of CNN and LSTM layers to detect the unwonted events in a video sequence. The proposed architecture is capable of exploiting both the spatial and temporal variation of video data. The reconstruction error is computed in terms of both MSE and PSNR for each testing video. A comparison is also carried out between MSE and PSNR to show that which assessment technique is better for a reconstructive model for recreating the video sequence. A well-optimized threshold is calculated which decides the fate of testing event i.e. either usual or unusual event. Using benchmark datasets, multiple experiments were carried out to demonstrate the efficacy of proposed architecture.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-13496-6