Surveillance Solutions on AI Edge

Organizations seeking real-time surveillance face critical challenges with conventional camera systems:

• High latency and bandwidth overload due to centralized processing, leading to delayed incident response.
• Absence of built-in AI, resulting in reliance on manual monitoring or costly cloud-based analytics.
• Privacy and data control concerns due to cloud-based storage and processing of sensitive footage.
• Scalability limitations when expanding across large areas or integrating additional devices.
• Monitoring inefficiencies from manual review under high camera volumes, increasing risk of oversight. 

• Edge Processing: Real-time video analysis directly on edge devices (NVIDIA Jetson, Qualcomm AI Box, Renesas).
• Integrated AI Models:
  • Traffic Counting.
  • People Counting.
  • Smoke & Fire Detection​​.
  • Helmet Detection​​.
  • Intrusion Detection.
  • Weapon Detection.
  • License Plate Recognition.
  • Face Recognition.
• Unified RTSP Camera Support: Compatible with standard IP cameras with 1080p+ resolution.
• Automated Alerts: Event-based alerts via sound, SMS, call, and visual dashboard.
• Management Platform:
  • User access control and audit logs.
  • Device and camera configuration.
  • Real-time map-based monitoring.
  • Custom dashboards and widgets.
• Secure Data Handling:
  • Encrypted video storage.
  • Role-based access to sensitive footage.
• Scalable Deployment:
  • Modular deployment in independent zones.
  • Integration with existing VMS (Video Management System)/CMS(Content Management System) platforms. 

• Low latency: Enables instant anomaly detection and rapid response at the source.
• Cost-effective: Reduces bandwidth consumption and manpower costs through localized processing.
• High accuracy: AI models trained for security-specific scenarios with up to 93% precision.
• Energy-saving hardware: Minimizes energy usage and extends device lifecycle with efficient edge components.
• Privacy-first design: Keeps sensitive data on-premises to ensure regulatory compliance and data sovereignty.
• Flexible deployment: Adapts to various environments—from industrial zones to educational campuses.
• Operational efficiency: Reduces false alarms and minimizes manual review workload.
• Future-ready: Supports easy AI model upgrades and seamless integration of new sensors or APIs.