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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Gulshan Saleem & Usama Ijaz Bajwa & Rana Hammad Raza & Fan Zhang, 2024. "Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments," Future Internet, MDPI, vol. 16(3), pages 1-17, February.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:3:p:83-:d:1348965
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