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

Havasi, László Rajmund and Szlávik, Zoltán and Szirányi, Tamás (2006) Higher order symmetry for non-linear classification of human walk detection. Pattern Recognition Letters, 27 (5). pp. 822-829.

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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 1-2 step periods. The detection rates of linear classifier and SVM are also presented in the results hereby the necessity of a nonlinear method and the power of KFDA for this detection task is also demonstrated.

Item Type: ISI Article
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Distributed Events Analysis Research Laboratory
Depositing User: Eszter Nagy
Date Deposited: 11 Dec 2012 15:27
Last Modified: 11 Dec 2012 15:27
URI: https://eprints.sztaki.hu/id/eprint/4798

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