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

  • Authors:

  • László Havasi;Zoltán Szlávik;Tamás Szirányi

  • Affiliations:

  • Péter Pázmány Catholic University, H-1083, Práter utca 50/A, Budapest, Hungary;Analogical and Neural Computing Laboratory, Computer and Automation Research Institute, Hungarian Academy of Sciences, H-1111 Budapest, Kende u. 13-17, Hungary;Analogical and Neural Computing Laboratory, Computer and Automation Research Institute, Hungarian Academy of Sciences, H-1111 Budapest, Kende u. 13-17, Hungary

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2006

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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.