Higher order symmetry for non-linear classification of human walk detection

László Havasi, Zoltán Szlávik, Tamás Szirányi

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

The paper focuses on motion-based information extraction from cluttered video image sequences. A novel method is introduced which can reliably detect walking human figures contained in such images. The method works with spatio-temporal input information to detect and classify patterns typical of human movement. Our algorithm consists of real-time operations, which is an important factor in practical applications. The paper presents a new information-extraction and temporal tracking method based on a simplified version of the symmetry-pattern extraction, which pattern is characteristic for the moving legs of a walking person. These spatio-temporal traces are labelled by kernel Fisher discriminant analysis. With the use of temporal tracking and non-linear classification we have achieved pedestrian detection from cluttered image scenes with a correct classification rate of 97.6% from 1 to 2 step periods. The detection rates of linear classifier and SVM are also presented in the results hereby the necessity of a non-linear method and the power of KFDA for this detection task is also demonstrated.

Original languageEnglish
Pages (from-to)822-829
Number of pages8
JournalPattern Recognition Letters
Volume27
Issue number7
DOIs
Publication statusPublished - May 2006

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Keywords

  • Gait analysis
  • Kernel Fisher discriminant analysis
  • Pedestrian detection
  • Simplified symmetry
  • Surveillance
  • Tracking

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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