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Updated: Jun 19, 2026

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

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Monocular pedestrian detection: survey and experiments.

Markus Enzweiler1, Dariu M Gavrila

  • 1Department of Mathematics and Computer Science, Image and Pattern Analysis Group, University of Heidelberg, Heidelberg, Germany. uni-heidelberg.enzweiler@daimler.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 17, 2009
PubMed
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This study compares pedestrian detection methods for intelligent vehicles. Wavelet-based AdaBoost excels at real-time speeds, while HOG/linSVM performs better at higher resolutions.

Area of Science:

  • Computer Vision
  • Robotics
  • Intelligent Vehicles

Background:

  • Pedestrian detection is crucial for autonomous systems.
  • Current methods require comprehensive evaluation.

Purpose of the Study:

  • To survey and experimentally evaluate state-of-the-art pedestrian detection algorithms.
  • To provide a benchmark dataset for future research.

Main Methods:

  • Survey of pedestrian detection system components and models.
  • Experimental comparison of Wavelet-based AdaBoost, HOG/linSVM, NN/LRF, and shape-texture methods.
  • Testing on a large dataset captured in urban driving conditions.

Main Results:

  • HOG/linSVM shows advantage at high resolutions and lower speeds.

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Last Updated: Jun 19, 2026

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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

  • Wavelet-based AdaBoost is superior at lower resolutions and real-time speeds.
  • A public dataset is released for benchmarking.

    • Conclusions:

    • Algorithm performance varies with resolution and speed requirements.
    • The study provides valuable insights for selecting pedestrian detection systems.
    • The public dataset facilitates further advancements in the field.