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Principal motion components for one-shot gesture recognition
Mar 05, 2017Author:
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Title: Principal motion components for one-shot gesture recognition

Authors: Escalante, HJ; Guyon, I; Athitsos, V; Jangyodsuk, P; Wan, J

Author Full Names: Escalante, Hugo Jair; Guyon, Isabelle; Athitsos, Vassilis; Jangyodsuk, Pat; Wan, Jun

Source: PATTERN ANALYSIS AND APPLICATIONS, 20 (1):167-182; 10.1007/s10044-015-0481-3 FEB 2017

Language: English

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 (TM) camera. Results obtained with PMC are competitive with alternative methods proposed for the same data set.

ISSN: 1433-7541

eISSN: 1433-755X

IDS Number: EJ2PR

Unique ID: WOS:000393053200012

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