Recently released depth cameras provide effective estimation of 3D positions of skeletal joints in temporal sequences of depth maps. In this work, we propose an efficient yet effective method to recognize human actions based on the positions of joints. First, the body skeleton is decomposed in a set of kinematic chains, and the position of each joint is expressed in a locally defined reference system which makes the coordinates invariant to body translations and rotations. A multi-part bag-of-poses approach is then defined, which permits the separate alignment of body parts through a nearest-neighbor classification. Experiments conducted on the Florence 3D Action dataset and the MSR Daily Activity dataset show promising results.
Recognizing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses / Lorenzo Seidenari;Vincenzo Varano;Stefano Berretti;Alberto Del Bimbo;Pietro Pala. - STAMPA. - (2013), pp. 479-485. (Intervento presentato al convegno International Conference on Computer Vision and Pattern Recognition tenutosi a Portland, Oregon, USA nel 23-28 June 2013) [10.1109/CVPRW.2013.77].
Recognizing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses
Lorenzo Seidenari;VARANO, VINCENZO;Stefano Berretti;Alberto Del Bimbo;Pietro Pala
2013
Abstract
Recently released depth cameras provide effective estimation of 3D positions of skeletal joints in temporal sequences of depth maps. In this work, we propose an efficient yet effective method to recognize human actions based on the positions of joints. First, the body skeleton is decomposed in a set of kinematic chains, and the position of each joint is expressed in a locally defined reference system which makes the coordinates invariant to body translations and rotations. A multi-part bag-of-poses approach is then defined, which permits the separate alignment of body parts through a nearest-neighbor classification. Experiments conducted on the Florence 3D Action dataset and the MSR Daily Activity dataset show promising results.File | Dimensione | Formato | |
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