Overview
This project developed an AI-powered safety video surveillance system designed to enhance operational security in chemical and industrial environments, particularly around static electricity grounding points. The system employs advanced computer vision and deep learning models to detect human presence, small body parts (hands, heads), and unsafe behaviors such as phone usage, ensuring real-time safety compliance in high-risk zones.
The core prototype has been completed and validated, and the system is currently being maintained and deployed in scalable cloud-based architectures for industrial use.
Technical Approach
- Deep Neural Networks for precise detection of hands, heads, and motion patterns.
- Action Recognition Models trained to identify phone-handling gestures and unsafe behavior.
- Edge–Cloud Architecture enabling low-latency response with scalable deployment.
- Alert and Response Mechanisms triggering safety notifications within seconds.
Research Areas Alignment
Computer Vision and AI Safety Systems; Cyber-Physical Integration bridging physical safety and digital monitoring; Industrial IoT and Cloud Infrastructure for scalable, distributed intelligent-manufacturing environments.
