Project Overview
A high-performance iOS and Android cloud video surveillance mobile application incorporating real-time computer vision AI event detection, multi-camera feeds, and instant emergency alerts.
Key Challenges & Objectives
Streaming high-definition video over cellular networks often suffers from buffering and battery drain. The app required low-latency RTSP/HLS stream rendering, intelligent AI motion classification to eliminate false alarms, and sub-second push notifications during security events.
Architecture & Implementation
Built with React Native and NativeWind (Tailwind CSS) for native cross-platform performance. Incorporates WebRTC hardware-accelerated video decoding, Firebase Cloud Messaging (FCM) high-priority push services, and edge-connected AI detection pipelines.
Core Features & Deliverables
- Multi-Camera Live Grid: Fluid multi-feed camera display with pinch-to-zoom and pan-tilt-zoom (PTZ) hardware controls.
- AI Anomaly Classification: Intelligent filtering recognizing people, vehicles, package deliveries, and unexpected motion while ignoring pets.
- Cloud Playback & Bookmarking: Scrubbable timeline with color-coded event markers and instant cloud clip export.
- Two-Way Audio Intercom: Low-latency bidirectional audio streaming to communicate through camera speakers remotely.
Business Impact & Results
Maintained sub-800ms alert delivery for security events, reduced false alarms by 82% through AI classification, and achieved 99.95% live stream uptime across thousands of deployed cameras.
