Parallel Symmetric Appearance‐Motion Framework With Diffusion and Refinement Blocks for Video Anomaly Detection System

ABSTRACT Video anomaly detection is crucial in network security, application performance monitoring, and quality control. It recognizes unexpected patterns or behaviors in video footage, allowing for threat detection, app optimization, and product quality improvement. Many deep learning models effec...

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Bibliographic Details
Published in:Concurrency and computation Vol. 37; no. 18-20
Main Authors: Prasad, Kavitapu Naga Siva Shankara Vara, Haritha, Dasari
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 30.08.2025
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Subjects:
Abnormalities
Anomalies
anomalous activities in videos
cross‐modality interaction
Decoding
Diffusion barriers
diffusion strengthening
Flow mapping
intermodal refinement block
motion‐ and appearance‐dominated branches
Optical flow (image analysis)
Pattern recognition
Quality control
residual inception‐based autoencoder
spatial and temporal autoencoder
UCI crime dataset
video anomaly detection
Video data
ISSN:1532-0626, 1532-0634
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Summary:ABSTRACT Video anomaly detection is crucial in network security, application performance monitoring, and quality control. It recognizes unexpected patterns or behaviors in video footage, allowing for threat detection, app optimization, and product quality improvement. Many deep learning models effectively detect video anomaly detection but have some limitations, such as more time computation and model complexity. To address these issues, this paper proposes the Parallel Symmetric Appearance‐Motion framework with Diffusion and Refinement blocks (PSAM‐DRB) for detecting video abnormalities. The proposed model's initial step is to pre‐process input videos to accentuate anomalous activities through video frame selection. Spatial and temporal Residual Inception‐based autoencoder extracts multi‐level features and optical flow maps in video frames. Feature decoding is performed using motion‐ and appearance‐dominated branches. A Diffusion Strengthening and Intermodal Refinement block enhances feature representation through cross‐scale augmentation and cross‐modality interaction. Finally, a fusion module combines the upper and lower branches to detect video anomalies. In this evaluation, the proposed model using the UCF‐Crime dataset achieved an accuracy of 99.19%. Finally, the proposed PSAM‐DRB framework provides a robust and efficient method for identifying anomalies in video data, with applications in a variety of industries.
Bibliography:The authors received no specific funding for this work.
Funding
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.70183