Operator Machine Augmentation Resource Framework

The growing number of people gathering in public and the massive incidents that have occurred in recent years. It raises questions about public safety and security. This paper illustrates the technical implementation of the operator machine augmentation resource (OMAR) framework, which integrates advanced technologies, including a Computer Vision model and CCTV operators’ training techniques, to address the limitations of traditional surveillance systems. The OMAR framework enhances the productivity of surveillance systems by facilitating operators’ tasks and improving theirs. The framework’s components, including Alert Triggers, a Computer Vision model, and human training, work together to create better output, and a more convincing system will improve the quality of security and reduce human effort. Although the OMAR framework represents a potentially significant step forward in surveillance security systems, it remains a theoretical model requiring further investigation and rigorous testing. Future work will focus on evaluating the effectiveness of the OMAR framework through an empirical study, examining its impact on various aspects of human performance and adaptations.

@article{Ameen2024,
  title     = {Operator Machine Augmentation Resource Framework},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mohammed Ameen and Richard Stone and Majed Hariri and Faisal Binzagr},
  doi       = {10.14569/IJACSA.2024.0150604},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150604}
}