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Smart Video Surveillance System Based on Edge Computing.

Antonio Carlos Cob-Parro1, Cristina Losada-Gutiérrez1, Marta Marrón-Romera1

  • 1Department of Electronics, University of Alcalá, Alcalá de Henares, 28801 Madrid, Spain.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a smart video surveillance system using artificial intelligence (AI) on low-power embedded devices. The system efficiently detects, counts, and tracks people, demonstrating excellent performance under resource constraints.

Keywords:
artificial intelligenceedge nodeembedded systemsmachine learningmobilenet-SSDvideo-surveillancevision processor unit

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Embedded Systems

Background:

  • Traditional computer vision algorithms are being replaced by advanced AI and deep learning methods.
  • High-performance AI systems typically require substantial data processing capabilities and large computing facilities.
  • There is a need for efficient AI-driven surveillance solutions on resource-constrained edge devices.

Purpose of the Study:

  • To develop and evaluate a smart video surveillance system utilizing AI algorithms on low-power embedded devices.
  • To enable real-time detection, counting, and tracking of people in surveillance areas.
  • To assess the system's performance, precision, and energy consumption under practical constraints.

Main Methods:

  • Implementation of a MobileNet-SSD architecture for people detection on edge devices.
  • Utilizing a Kalman filter bank for robust people tracking and counting.
  • Deployment on an UpSquared2 edge node with a vision processing unit (VPU) for AI acceleration.
  • Evaluation of image processing time, detection accuracy (precision/recall), and energy consumption.

Main Results:

  • The proposed AI application successfully detects people within the surveillance area.
  • The system achieves excellent detection results despite the constraints of embedded devices.
  • Performance metrics including processing time, detection precision, recall, and energy consumption were analyzed for multi-camera setups.

Conclusions:

  • The developed smart camera node is effective for distributed surveillance systems.
  • Deploying AI algorithms on low-power embedded devices is feasible and efficient for surveillance tasks.
  • The system offers a practical solution for intelligent video surveillance with reduced computational overhead.