Review on Deep Learning-Based Approach to Intelligent Video Surveillance Systems

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@article{1709475,
      author = {Ettebong Stephen James, Kingsley M. Udofia, Akaninyene B. Obot},
      title = {Review on Deep Learning-Based Approach to Intelligent Video Surveillance Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {1},
      pages = {321-329},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709475.pdf},
      abstract = {In recent years, the demand for more robust and Intelligent Video Surveillance Systems (IVSS) has grown due to the increasing need for public safety and security in both urban and remote environments. This study investigates the application of various techniques like Deep Learning (DL) and Machine Learning (ML) techniques in enhancing video surveillance systems considering anomaly detection, human behaviour recognition, violence detection and weapon identification. A comprehensive literature review was conducted for the assessment of performance, advantages and limitations of existing intelligent surveillance systems which highlighted that the capabilities of advanced models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and hybrid deep learning architectures in automatically analysing video footage, learning complex patterns and detecting threats in dynamic environments significantly outperform traditional methods in terms of accuracy, adaptability and operational efficiency. The study concludes that the integration of DL and ML into surveillance systems presents a promising direction for modern security infrastructure, which not only reduces the burden on human operators but also enhance real-time threat detection and response, making them indispensable tools for future surveillance applications.},
      keywords = {Video Surveillance; Deep Learning (DL); Machine Learning (ML); CNN; RNN; Human Behaviour},
      month = {July},
  }