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Continuous hand gesture recognition based on trajectory shape information
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DOI：10.1016/j.patrec.2017.05.016.png)
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In this paper, we propose a continuous hand gesture recognition method based on trajectory shape information. A key issue in recognizing continuous gestures is that performance of conventional recognition algorithms may be lowered by such factors as, unknown start and end points of a gesture or variations in gesture duration. These issues become particularly difficult for those methods that rely on temporal information. To alleviate the issues of continuous gesture recognition, we propose a framework that simultaneously performs both segmentation and recognition. Each component of the framework applies shape-based information to ensure robust performance for gestures with large temporal variation. A gesture trajectory is divided by a set of key frames by thresholding its tangential angular change. Variablesized trajectory segments are then generated using the selected key frames. For recognition, these trajectory segments are examined to determine whether the segment belongs to a class among intended gestures or a non-gesture class based on fusion of shape information and temporal features. In order to assess performance, the proposed algorithm was evaluated with a database of digit hand gestures. The experimental results indicate that the proposed algorithm has a high recognition rate while maintaining its performance in the presence of continuous gestures. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Gesture recognition
Human robot interaction
Convolution neural network
Conditional random fields
Trajectory segmentation
Trajectory shape modeling
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被引数:
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