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Intelligent Surveillance with LiDAR-Based Motion Tracking

We developed an Edge AI surveillance system that uses LiDAR and depth sensors to record only meaningful activity, minimizing bandwidth and storage needs.
 By integrating computer vision and depth analysis, the system enables accurate, efficient, and privacy-conscious monitoring for smart infrastructure and industrial environments.

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Summary :

We have built a LiDAR-based intelligent surveillance solution that detects and records activity only when it occurs. By combining depth sensing, point cloud processing, and AI-based object detection, the system distinguishes between real movement and environmental noise. This approach drastically reduces storage and bandwidth consumption while improving accuracy and operational efficiency in large or remote surveillance setups.

Problem

Problem

Objectives

  • Reduce Storage & Bandwidth Usage

    Record only when activity is detected through LiDAR and AI verification.

  • Enhance Detection Precision

    Differentiate between real activity and environmental noise.

  • Enable Edge Deployment

    Process data locally to minimize cloud dependency

  • Improve Operational Efficiency

    Optimize recording and monitoring for large or remote sites.

  • Ensure Scalable Integration

    Support multiple depth sensors and LiDAR hardware.

Challenges

  • Processing and interpreting real-time LiDAR point cloud data efficiently.
  • Synchronizing LiDAR and camera streams for unified motion detection.
  • Managing large data streams on low-power edge devices.
  • Reducing false triggers caused by non-human movement (e.g., wind, shadows).
  • Ensuring accurate detection under changing light and weather conditions.

Solution

  • Designed an AI and LiDAR-based motion tracking system to enable event-driven video recording.
  • Integrated LiDAR and depth sensors (Intel RealSense, Ouster) for 3D spatial awareness.
  • Applied point cloud processing in C++ to detect motion and calculate precise object depth.
  • Combined motion tracking with AI-based validation using YOLO models to confirm human or vehicle activity.
  • Optimized the system for edge performance, reducing network dependency.
  • Recorded video only when verified motion occurred, saving bandwidth and storage.
  • Delivered a flexible architecture that supports multi-sensor integration for industrial and public safety environments.

Architecture

Depth Capture
LiDAR and depth cameras capture real-time 3D spatial data.
Motion Analysis
AI analyzes point clouds to detect meaningful movement.
Event Validation
Object detection confirms human or vehicle activity
Conditional Recording
System records and stores video only when validated events occur.
Data Storage & Review
Compressed, event-based footage stored for review and analytics.

Results & Impact

Reduced storage and bandwidth usage by over 80%

Enabled intelligent, event-driven recording for surveillance

Delivered privacy-focused monitoring by avoiding unnecessary video capture

Optimized edge processing for real-time performance

Deployed at scale for smart infrastructure and remote facilities

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