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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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An Evaluation of the Pedestrian Classification in a Multi-Domain Multi-Modality Setup.

Alina Miron1, Alexandrina Rogozan2, Samia Ainouz3

  • 1ISR Laboratory, University of Reading, Reading RG6 6AY, UK. a.d.miron@reading.ac.uk.

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Summary

This study compares pedestrian classification using visible and far-infrared (FIR) light. Far-infrared imaging proved superior, but fusing data from both domains and multiple features yielded the best pedestrian detection results.

Keywords:
benchmarkfeature comparisoninfrared pedestrian classificationintensity self-similaritymulti-cuemulti-domainmulti-modalitystereovision

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Sensor Fusion

Background:

  • Pedestrian classification is crucial for autonomous systems.
  • Existing methods for visible and far-infrared (FIR) domains are difficult to compare due to dataset limitations.
  • A unified dataset and comparative analysis are needed for robust pedestrian detection.

Purpose of the Study:

  • To introduce a public dataset (RIFIR) for comparing pedestrian classification across visible and FIR domains.
  • To evaluate state-of-the-art features (ISS, LBP, LGP, HOG) in a multi-modality setup (intensity, depth, motion).
  • To compare performance across different light spectrum domains and modalities for pedestrian detection.

Main Methods:

  • Collected a new public dataset (RIFIR) with synchronized visible and FIR images from a moving vehicle.
  • Extracted and compared intensity self-similarity (ISS), local binary patterns (LBP), local gradient patterns (LGP), and histogram of oriented gradients (HOG) features.
  • Evaluated features across visible and FIR domains, considering intensity, depth, and motion modalities.

Main Results:

  • FIR domain generally outperformed the visible domain for pedestrian classification.
  • Features from FIR and visible domains were found to be highly complementary.
  • The best pedestrian detection performance was achieved through multi-domain, multi-modality, and multi-feature fusion.

Conclusions:

  • The RIFIR dataset enables standardized comparison of pedestrian detection algorithms.
  • Multi-modal and multi-domain fusion significantly enhances pedestrian classification accuracy.
  • Far-infrared imaging offers advantages for pedestrian detection, especially when combined with visible light data.