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

Modern human action recognition algorithms which exploit 3D information mainly classify video sequences by extracting 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.