Real-Time Monitoring and Analysis Through Video Surveillance and Alert Generation for Prompt and Immediate Response

The efficacy of Closed-Circuit Television systems (CCTV) in residential areas is often linked to the lack of real-time alerts and rapid response mechanisms. Enabling immediate notifications upon the identification of irregularities or aggressive conduct can greatly enhance the possibility of averting serious incidents, or at the very least, significantly mitigate their impact. The integration of an automated system for anomaly detection and monitoring, augmented by a real-time alert mechanism, is now a critical necessity. The proposed work presents an advanced methodology for real-time detection of accidents and violent activities, incorporating a sophisticated alarm system that not only triggers instant alerts but also captures and stores video frames for detailed post-event analysis. MobileNetV2 is utilized for spatial analysis due to its computational efficiency compared to other Convolutional Neural Networks (CNN) architectures, while Visual Geometry Group 16 (VGG16) enhances model accuracy, especially on large-scale datasets. The integration of Bi-directional Long Short-Term Memory (BiLSTM) strengthens temporal continuity, significantly reducing false alarms. The proposed system aims to improve both safety and security by enabling authorities to intervene timely to incidents. Combining rapid computation with high detection accuracy, the proposed model is ideally suited for real-time deployment across both urban and residential settings.

@article{Kumar2024,
  title     = {Real-Time Monitoring and Analysis Through Video Surveillance and Alert Generation for Prompt and Immediate Response},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Akshat Kumar and Renuka Agrawal and Akshra Singh and Aaftab Noorani and Yashika Jaiswal and Preeti Hemnani and Safa Hamdare},
  doi       = {10.14569/IJACSA.2024.0151272},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151272}
}