Human limb segmentation in depth maps based on spatio-temporal Graph-cuts optimization

Authors:
Antonio Hernández-Vela;Nadezhda Zlateva;Alexander Marinov;Miguel Reyes;Petia Radeva;Dimo Dimov;Sergio Escalera
Affiliations:
Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra Cerdanyola, Barcelona, Spain. E-mail: {ahernandez,mreyes,petia,sescalera}@cvc.uab.cat and Dept. of Applied Mathematics and Analysis, ...;Inst. of Information and Communication Technologies, BAS, Acad. G. Bonchev St., Block 2, Sofia 1113, Bulgaria. E-mail: {zlateva,amarinov,dtdim}@iinf.bas.bg;Inst. of Information and Communication Technologies, BAS, Acad. G. Bonchev St., Block 2, Sofia 1113, Bulgaria. E-mail: {zlateva,amarinov,dtdim}@iinf.bas.bg;Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra Cerdanyola, Barcelona, Spain. E-mail: {ahernandez,mreyes,petia,sescalera}@cvc.uab.cat and Dept. of Applied Mathematics and Analysis, ...;Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra Cerdanyola, Barcelona, Spain. E-mail: {ahernandez,mreyes,petia,sescalera}@cvc.uab.cat and Dept. of Applied Mathematics and Analysis, ...;Inst. of Information and Communication Technologies, BAS, Acad. G. Bonchev St., Block 2, Sofia 1113, Bulgaria. E-mail: {zlateva,amarinov,dtdim}@iinf.bas.bg;Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra Cerdanyola, Barcelona, Spain. E-mail: {ahernandez,mreyes,petia,sescalera}@cvc.uab.cat and Dept. of Applied Mathematics and Analysis, ...
Venue:
Journal of Ambient Intelligence and Smart Environments
Year:
2012

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Abstract

We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches.