Project Overview:
Dyseye was developed as an AI-based visual analytics platform to enhance object and behavior recognition in complex environments such as airports, industrial facilities, and urban intersections. The primary goal was to create a solution that could “see and interpret” with near-human accuracy.
Solution:
Using deep learning algorithms, Dyseye was trained on vast datasets to identify unusual behavior, unattended objects, and safety violations in real-time. It seamlessly integrated with existing CCTV infrastructure and offered predictive insights through a smart dashboard. The system also included an alert mechanism for automated notifications to relevant stakeholders.
Outcome:
Dyseye successfully improved incident detection rates by over 70% compared to legacy systems. In industrial settings, it reduced manual monitoring efforts by 50%, freeing up human resources for higher-value tasks. Its predictive alerts helped prevent several potential safety hazards, earning praise from multiple public and private sector clients.
