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Reducing False Positives in Motion Sensors with Edge AI

We solved one of the most persistent challenges in motion detection, false alarms triggered by irrelevant movement, using Edge AI and object detection.
 The system intelligently filters real motion events from environmental noise, ensuring accurate detection and reliable alerts even in dynamic outdoor conditions.

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Summary :

We have built an Edge AI solution that combines traditional motion sensors with computer vision to verify real movement in real time. By integrating object detection models directly on edge devices, the system distinguishes between meaningful activity (like humans or animals) and false triggers (such as wind, shadows, or lighting changes). This hybrid approach reduces false positives, improves reliability, and enables efficient surveillance and wildlife monitoring in remote areas.

Problem

Problem

Objectives

  • Eliminate False Positives

    Reduce false motion alerts by combining sensor data with AI-based object detection.

  • Improve Detection Accuracy

    Identify valid objects such as humans, animals, or vehicles with high confidence.

  • Optimize for Edge Devices

    Run real-time AI inference on low-power hardware like Qualcomm QCS610.

  • Ensure Field Reliability

    Maintain consistent performance under variable outdoor conditions.

  • Enhance Operational Efficiency

    Reduce bandwidth and manual verification effort.

Challenges

  • Achieving stable performance on low-power edge processors.
  • Differentiating between relevant and irrelevant motion under fluctuating lighting.
  • Maintaining low latency for real-time alerts.
  • Optimizing AI inference for embedded hardware with limited compute capacity.
  • Designing a model architecture that minimizes energy usage without sacrificing accuracy.

Solution

  • Developed a hybrid Edge AI system that combines motion sensor input with real-time object detection for validation.
  • Used YOLOv5s-based detection models to identify relevant motion types (human, animal, or vehicle).
  • Deployed and optimized the model on Qualcomm QCS610 using C++ and xtensor for edge-level inference.
  • Integrated a verification pipeline where AI confirms actual activity before alert generation.
  • Achieved high accuracy in distinguishing meaningful events from background noise.

Architecture

Sensor Trigger
Motion sensor activates when any movement is detected.
AI Verification
Edge AI model processes frames to classify motion as valid or false.
Event Validation
Only verified motion triggers alerts and recording.
Local Processing
Data is analyzed on-device, minimizing cloud dependency.
Output
Accurate detection logs and event summaries for monitoring dashboards.

Results & Impact

Reduced false positives by filtering out non-relevant motion events

Improved alert accuracy, ensuring only meaningful events are flagged

Enhanced energy efficiency by minimizing unnecessary sensor activation

Enabled reliable field deployment for wildlife and outdoor surveillance

Delivered real-time AI processing on edge devices without cloud dependence

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