Abstract
Gesture recognition is of great significance for human-machine interaction and it has broad application prospects. In order to improve the detection accuracy and speed, a real-time recognition method of static gesture based on Deconvolutional Single Shot Detector (DSSD) is proposed in this paper. We have improved the original DSSD network and the deconvolution module, used the K-means clustering algorithm to select the aspect ratios of the prior boxes to improve the detection accuracy. The detection accuracy of small data set is improved by introducing transfer learning method, and the influences of three different base networks on the DSSD network model are discussed. In order to verify the effectiveness of the proposed method, we compared it with the gesture recognition methods based on SSD300, SSD321, YOLOV2 and DES in ASL dataset. The experimental results show that the proposed method has a recognition rate of 94.8%, which is 2.7%, 2.1% and 2.8%higher than SSD300, SSD321 and YOLOv2, respectively. The detection rate is close to the method of Single-Shot Object Detection with Enriched Semantics (DES), while still maintaining a reasonable detection speed of 27 FPS. In addition, since DSSD fuse the semantic information of each feature extraction layer, the proposed method also has good detection ability for small gesture objects.
Published Version
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