Principal motion components for one-shot gesture recognition

Isabelle Guyon 1, 2 Hugo Jair Escalante 3, 2 Vassilis Athitsos 4 Pat Jangyodsuk 4 Jun Wan 5
1 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : This paper introduces principal motion components (PMC), a new method for one-shot gesture recognition. In the considered scenario a single training video is available for each gesture to be recognized, which limits the application of traditional techniques (e.g., HMMs). In PMC, a 2D map of motion energy is obtained per each pair of consecutive frames in a video. Motion maps associated to a video are processed to obtain a PCA model, which is used for recognition under a reconstruction-error approach. The main benefits of the proposed approach are its simplicity, easiness of implementation, competitive performance and efficiency. We report experimental results in one-shot gesture recognition using the ChaLearn Gesture Dataset; a benchmark comprising more than 50,000 gestures, recorded as both RGB and depth video with a Kinect™camera. Results obtained with PMC are competitive with alternative methods proposed for the same data set.
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Contributeur : Isabelle Guyon <>
Soumis le : lundi 8 janvier 2018 - 17:02:30
Dernière modification le : jeudi 7 février 2019 - 15:13:21



Isabelle Guyon, Hugo Jair Escalante, Vassilis Athitsos, Pat Jangyodsuk, Jun Wan. Principal motion components for one-shot gesture recognition. Pattern Analysis and Applications, Springer Verlag, 2017, 20 (1), pp.167 - 182. 〈10.1007/s10044-015-0481-3〉. 〈hal-01677941〉



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