Modern human action recognition algorithms which exploit 3D information mainly classify video sequences by extract- ing local or global features from the RGB-D domain or classifying the skeleton information provided by a skeletal tracker. In this paper, we propose a comparison between two techniques which share the same classification process, while differing in the type of descriptor which is classified. The former exploits an improved version of a recently proposed approach for 3D motion flow estimation from colored point clouds, while the latter relies on the estimated skeleton joints positions. We compare these methods on a newly created dataset for RGB-D human action recognition which contains 15 actions performed by 12 different people.

An evaluation of 3D motion flow and 3D pose estimation for human action recognition

MUNARO, MATTEO;MICHIELETTO, STEFANO;MENEGATTI, EMANUELE
2013

Abstract

Modern human action recognition algorithms which exploit 3D information mainly classify video sequences by extract- ing local or global features from the RGB-D domain or classifying the skeleton information provided by a skeletal tracker. In this paper, we propose a comparison between two techniques which share the same classification process, while differing in the type of descriptor which is classified. The former exploits an improved version of a recently proposed approach for 3D motion flow estimation from colored point clouds, while the latter relies on the estimated skeleton joints positions. We compare these methods on a newly created dataset for RGB-D human action recognition which contains 15 actions performed by 12 different people.
2013
RSS Workshops: RGB-D: Advanced Reasoning with Depth Cameras.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2718093
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