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Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case.

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  • 1Algorithmic Center, University of Minho, 4800-058 Azurém, Portugal.

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Summary

This study explores sensor fusion for indoor multi-human tracking. Combining grayscale and neuromorphic vision sensor (NVS) data with deep learning improves motion detection, with optimal features depending on data availability.

Keywords:
event-based dataindoor surveillancemulti-modal datamultiple human motion detection and trackingneuromorphic vision sensorsensor fusion

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Multi-human detection and tracking in indoor surveillance presents significant challenges.
  • Occlusions, varying illumination, and complex interactions hinder performance.
  • Existing methods often struggle with these dynamic environmental factors.

Purpose of the Study:

  • To investigate the efficacy of low-level sensor fusion for enhancing multi-human detection and tracking.
  • To compare the performance of different input features and deep learning architectures.
  • To determine optimal sensor fusion strategies for indoor surveillance applications.

Main Methods:

  • A custom dataset was generated using a neuromorphic vision sensor (NVS) camera in an indoor setting.
  • Experiments involved various image features and deep learning networks.
  • A multi-input fusion strategy was employed to optimize for overfitting and analyze input feature importance.

Main Results:

  • Significant differences were observed in the performance of input features across optimized deep learning backbones.
  • Under low-data conditions, event-based frames from NVS were found to be superior.
  • With higher data availability, the combination of grayscale and optical flow features yielded the best results.

Conclusions:

  • Sensor fusion, particularly combining grayscale and NVS data, shows significant potential for improving multi-human tracking in indoor surveillance.
  • The optimal feature selection is data-dependent, highlighting the need for adaptive strategies.
  • Further research is recommended to validate and extend these findings.