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Recognition of Non-pedestrian Human Forms Through Locally Weighted Descriptors

  • Nancy Arana-DanielEmail author
  • Isabel Cibrian-Decena
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)

Abstract

To recognize human forms in non-pedestrian poses presents a high complexity problem due mainly to the large number of degrees of freedom of the human body and its limbs. In this paper it is proposed a methodology to build and classify descriptors of non-pedestrian human body forms in images which is formed with local and global information. Local information is obtained by computing Local Binary Pattern (LBP) of key-body parts (head-shoulders, hands, feet, crotch-hips) detected in the image in a first stage of the method, and then this data is coupled in the descriptor with global information about euclidean distances computed between the key-body parts recognized in the image. The descriptor is then classified using a Support Vector Machine. The results obtained using the proposed recognition methodology show that it is robust to partial occlusion of bodies, furthermore the values of sensitivity, accuracy and specificity of the classifier are high enough compared with those obtained using other state of the art descriptors.

Keywords

Human detection Non-pedestrian pose Local Binary Pattern (LBP) Support Vector Machine (SVM) 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e IngenieríasGuadalajaraMexico

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