Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

Gulshan Saleem, Usama Ijaz Bajwa (), Rana Hammad Raza and Fan Zhang ()
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Gulshan Saleem: Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan
Usama Ijaz Bajwa: Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan
Rana Hammad Raza: Electronics and Power Engineering Department, Pakistan Navy Engineering College (PNEC), National University of Sciences and Technology (NUST), Karachi 75350, Pakistan
Fan Zhang: Ocean College, Zhejiang University, Hangzhou 316000, China

Future Internet, 2024, vol. 16, issue 3, 1-17

Abstract: Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNet, a cutting-edge framework that strategically reduces spatial resolution to allow the processing of low-resolution images. A dual upscaling methodology based on bicubic interpolation and an encoder–bank–decoder configuration is used for anomaly classification. The two-stream architecture combines the power of a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction from RGB imagery in the spatial stream, while the temporal stream focuses on learning short-term temporal characteristics, reducing the computational burden of optical flow. To analyze long-term temporal patterns, the extracted features from both streams are combined and routed through a Gated Recurrent Unit (GRU) layer. The proposed framework (TempoFuseNet) outperforms the encoder–bank–decoder model in terms of performance metrics, achieving a multiclass macro average accuracy of 92.28%, an F1-score of 69.29%, and a false positive rate of 4.41%. This study presents a significant advancement in the field of video anomaly recognition and provides a comprehensive solution to the complex challenges posed by real-world surveillance scenarios in the context of 5G and IoT.

Keywords: edge intelligence; anomaly identification; super resolution; video classification; two-stream architecture; StyleGAN; IoT environment (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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