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Moving Object Detection in Heterogeneous Conditions in Embedded Systems.

Alessandro Garbo1, Stefano Quer2

  • 1Dipartimento di Automatica ed Informatica, Politecnico di Torino, 10129 Torino, Italy. alessandro.garbo@polito.it.

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Summary
This summary is machine-generated.

This study introduces an efficient system for real-time pedestrian detection in challenging environments. The method merges video frames using combined movement detection and tracking, achieving high accuracy on low-cost hardware.

Keywords:
automatic surveillanceembedded systemshuman detectionmotion estimationtracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Embedded Systems

Background:

  • Pedestrian detection is crucial for intelligent transportation systems.
  • Existing methods often struggle in complex, real-world conditions.
  • Resource-constrained embedded devices require efficient algorithms.

Purpose of the Study:

  • To develop a robust and accurate pedestrian detection system for heterogeneous environments.
  • To optimize performance for real-time applications on inexpensive hardware.
  • To present a novel orchestration of existing movement detection and tracking techniques.

Main Methods:

  • Merging information from sequential video frames with minimal computational load per frame.
  • Employing dynamically adjusted thresholds for region-of-interest characterization.
  • Integrating multiple movement detection and tracking algorithms with intelligent orchestration.
  • Implementing techniques for efficient movement tracking, detection, and false positive correction.

Main Results:

  • The system demonstrates robustness and accuracy across diverse video sequences.
  • Achieved comparable tracking accuracy to state-of-the-art methods.
  • Significantly higher frame-per-second rates compared to existing strategies.
  • Validated performance on inexpensive hardware, suitable for intelligent urban grids.

Conclusions:

  • The presented system offers an effective solution for real-time pedestrian detection in challenging environments.
  • It provides a valuable tool for embedded applications with limited computational resources.
  • The intelligent combination of algorithms and efficient processing enables high performance on low-cost devices.