This paper describes a method to estimate the body pose of a human from the point cloud obtained from a depth sensor. It uses Differential Evolution to find the best match between a candidate pose, represented by an instance of a 42-parameter articulated model of a human, and the point cloud. The results, compared to other four state-of-the art methods on a publicly available dataset, show that the method has good ability to estimate the pose of a person and to track him in video sequences. The entire method, from Differential Evolution to fitness computation, is run on nVIDIA GPUs. Thanks to its massively parallel implementation in CUDA-C, it produces pose estimates in real time.

Differential Evolution Based Human Body Pose Estimation from Point Clouds / Ugolotti, Roberto; Cagnoni, Stefano. - ELETTRONICO. - (2013), pp. 1389-1396. ((Intervento presentato al convegno Proc. Genetic and Evolutionary Computation Conference (GECCO `13) tenutosi a Amsterdam nel 6-10 luglio [10.1145/2463372.2463528].

Differential Evolution Based Human Body Pose Estimation from Point Clouds

UGOLOTTI, Roberto;CAGNONI, Stefano
2013-01-01

Abstract

This paper describes a method to estimate the body pose of a human from the point cloud obtained from a depth sensor. It uses Differential Evolution to find the best match between a candidate pose, represented by an instance of a 42-parameter articulated model of a human, and the point cloud. The results, compared to other four state-of-the art methods on a publicly available dataset, show that the method has good ability to estimate the pose of a person and to track him in video sequences. The entire method, from Differential Evolution to fitness computation, is run on nVIDIA GPUs. Thanks to its massively parallel implementation in CUDA-C, it produces pose estimates in real time.
9781450319638
Differential Evolution Based Human Body Pose Estimation from Point Clouds / Ugolotti, Roberto; Cagnoni, Stefano. - ELETTRONICO. - (2013), pp. 1389-1396. ((Intervento presentato al convegno Proc. Genetic and Evolutionary Computation Conference (GECCO `13) tenutosi a Amsterdam nel 6-10 luglio [10.1145/2463372.2463528].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2616650
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