Relational graph matching for human detection and posture recognition

Ibrahim Burak Ozer; Wayne H. Wolf; Ali Naci Akansu

doi:10.1117/12.403798

11 October 2000 Relational graph matching for human detection and posture recognition

Ibrahim Burak Ozer, Wayne H. Wolf, Ali Naci Akansu

Proceedings Volume 4210, Internet Multimedia Management Systems; (2000) https://doi.org/10.1117/12.403798
Event: Information Technologies 2000, 2000, Boston, MA, United States

Abstract

This paper describes a relational graph matching with model-based segmentation for human detection. The matching result is used for the decision of human presence in the image as well as for posture recognition. We extend our previous work for rigid object detection in still images and video frames by modeling parts with superellipses and by using multi-dimensional Bayes classification in order to determine the non-rigid body parts under the assumption that the unary and binary (relational) features belonging to the corresponding parts are Gaussian distributed. The major contribution of the proposed method is to create automatically semantic segments from the combination of low level edge or region based segments using model-based segmentation. The generality of the reference model part attributes allows detection of human with different postures while the conditional rule generation decreases the rate of false alarms.

Citation Download Citation

Ibrahim Burak Ozer, Wayne H. Wolf, and Ali Naci Akansu "Relational graph matching for human detection and posture recognition", Proc. SPIE 4210, Internet Multimedia Management Systems, (11 October 2000); https://doi.org/10.1117/12.403798

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