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A high accuracy pedestrian detection system combining a cascade AdaBoost detector and random vector functional-link

Zhihui Wang1, Sook Yoon2, Shan Juan Xie3

  • 1Department of Electronics Engineering, Chonbuk National University, Jeonju 561-756, Republic of Korea.

Thescientificworldjournal
|June 25, 2014
PubMed
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This study introduces an accurate pedestrian detection system using cascade AdaBoost and random vector functional-link networks to reduce false positives. The combined machine learning approach enhances detection accuracy for reliable pedestrian identification.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Pedestrian detection systems often achieve high accuracy at the expense of numerous false positive detections.
  • Existing single machine learning algorithms struggle to balance detection accuracy with false positive rates.

Purpose of the Study:

  • To develop an accurate pedestrian detection system that minimizes false positive detections.
  • To integrate cascade AdaBoost detectors and random vector functional-link networks for improved performance.

Main Methods:

  • Utilized a multiscale sliding window strategy for candidate extraction.
  • Employed offline training of cascade AdaBoost detector and random vector functional-link network parameters on a standard dataset.
  • Implemented online verification of normalized candidates using both trained machine learning models.

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Main Results:

  • The proposed system demonstrated higher accuracy compared to single machine learning algorithms.
  • The integrated approach significantly reduced the number of false pedestrian detections.
  • Performance was validated through simulation experiments across four distinct datasets.

Conclusions:

  • The combined use of cascade AdaBoost and random vector functional-link networks offers a superior solution for accurate pedestrian detection.
  • This method effectively addresses the challenge of high false positive rates in current systems.
  • The system shows promise for real-world applications requiring reliable pedestrian identification.