Relational graph matching for human detection and posture recognition

摘要

This ppaer describes a relational graph matching with model-based wsegmentation for huyman 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 supperellipese 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 for the combination of low level edge or region based segment suing model-based segmentation. The generality of the reference model part attributes allows deection of human with different postures while the conditional rule generation decreases the rate of false alarms.