Collaborative voting of 3D features for robust gesture estimation
Document typeConference lecture
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each selected point (anchor point) contributes to the estimation by casting votes. The main idea is to detect a body part by accumulating the inference of other trained body parts. The collaborative voting encodes the global context of human pose, while 3D features represent local appearance. Body parts contributing to the detection are interpreted as a voting process. Experimental results for different 3D features prove the validity of the proposed algorithm.
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Citationvan Sabben, D. [et al.]. Collaborative voting of 3D features for robust gesture estimation. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017): New Orleans, Louisiana, USA: 5-9 March 2017". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1677-1681.