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Journal Articles Pattern Analysis and Applications Year : 2017

Principal motion components for one-shot gesture recognition


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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Dates and versions

hal-01677941 , version 1 (08-01-2018)



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