access icon free LSTM-based dynamic probability continuous hand gesture trajectory recognition

© The Institution of Engineering and Technology
Received 27/05/2019, Accepted 29/07/2019, Published 07/08/2019

In the field of continuous hand-gesture trajectory recognition, aiming at the problems of existing a lot of noise for handwriting trajectories, and difficult to segment multiple continuous hand gestures accurately, a long short-term memory-based dynamic probability (DP-LSTM) method is proposed. Firstly, obtain the classification result for each sub-period in the whole time period by using LSTM; secondly, cluster the classification results by non-maximum suppression for trajectory algorithm to eliminate interference of invalid subsets; Finally, the end point of the valid trajectory is obtained according to the characteristics of the probability change, thus realising dynamic trajectory segmentation and recognition. In order to evaluate the performance of the DP-LSTM, this method is evaluated by using an Arabic numerals gesture database. The experiments show that the DP-LSTM has a high recognition rate for continuous hand gestures and can recognise its in real time.

Inspec keywords: image segmentation; feature extraction; object detection; probability; gesture recognition

Other keywords: short-term memory-based dynamic probability method; trajectory algorithm; handwriting trajectories; probability change; Arabic numerals gesture database; valid trajectory; dynamic trajectory segmentation; continuous hand-gesture trajectory recognition; classification result; LSTM-based dynamic probability continuous hand gesture trajectory recognition; DP-LSTM; segment multiple continuous hand gestures

Subjects: Other topics in statistics; Computer vision and image processing techniques; Optical, image and video signal processing; Image recognition; Other topics in statistics

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