EyeSense is an enterprise-grade, real-time AI surveillance system designed for automated visitor monitoring and security tracking. The system replaces manual check-in processes by utilizing computer vision techniques to detect, track, and recognize individuals across multiple concurrent video streams, reducing manual security workloads by approximately 60%.

Key Features & Technical Architecture

Multi-Stream Processing: Engineered a low-latency video pipeline using OpenCV operating at 25 FPS, capable of processing 4 active CCTV feeds concurrently without frame drops or memory leaks.

Face Recognition & Indexing: Implemented identity matching by extracting high-dimensional embeddings using HuggingFace FaceNet models and indexing them into a FAISS vector database. Achieved sub-100ms vector matching across a database of 500+ identity profiles.

Automated Alerting Engine: Designed a real-time event engine that flags unrecognized or unauthorized individuals within 3 seconds, automatically triggering instant security alerts via SMS and email.

Management Dashboard & Containerization: Developed a Flask-based web platform for system administration, access logging, real-time visitor approval, and monitoring. Containerized the entire application stack using Docker for standardized deployment across hardware environments.