Crowd management, crime detection, work monitoring using aiml
This addresses security and efficiency issues for public safety and organizational stakeholders, but it appears incremental as it applies existing AI/ML methods to existing CCTV networks.
This research tackled the problem of enhancing surveillance by using AI/ML to analyze CCTV video feeds for crowd management, crime detection, and work monitoring, aiming to improve public safety and productivity without requiring major infrastructure changes.
This research endeavors to harness the potential of existing Closed-Circuit Television (CCTV) networks for a comprehensive approach to crowd management, crime prevention, and workplace monitoring through the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. The primary objective is to develop and implement advanced algorithms capable of real-time analysis of video feeds, enabling the identification and assessment of crowd dynamics, early detection of potential criminal activities, and continuous monitoring of workplace environments. By leveraging AI/ML, the project aims to optimize surveillance capabilities, thereby enhancing public safety measures and improving organizational productivity. This initiative underscores the transformative impact that intelligent video analytics can have on existing infrastructure, mitigating the need for extensive system overhauls while significantly advancing security and operational efficiency.