Abstract
The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic contour image is initialized. The residual network method is used to accurately identify the contour and texture features of dynamic gestures. Fusion processing weights are used to trace the contour features of dynamic gestures frame by frame, and the contour area of dynamic gestures is processed by gray and binarization to realize the extraction of contour features of dynamic gestures. The results show that the dynamic gesture contour feature recognition rate of the proposed method is 91%, the recognition time is 11.6 s, and the dynamic gesture type recognition accuracy rate is 92%. Therefore, this method can effectively improve the recognition rate and type recognition accuracy of dynamic gesture contour features and shorten the time for dynamic gesture contour feature recognition, and the F value is 0.92, with good comprehensive performance.
Highlights
Gesture is an intuitive and convenient way of communication, and natural and comfortable human-computer interaction can be realized through gesture recognition [1]
To solve the above problems, this paper proposes a dynamic gesture contour feature extraction method based on residual network transfer learning
The results show that the proposed method has high dynamic gesture contour feature recognition rate and gesture type recognition accuracy rate, and the recognition time is short, only 11.6 s, and the average F value of feature extraction is as high as 0.92
Summary
Gesture is an intuitive and convenient way of communication, and natural and comfortable human-computer interaction can be realized through gesture recognition [1]. Dynamic gestures are more intuitive than static gestures and are widely used in flexible human-computer interaction applications. It can manipulate virtual objects in a virtual reality environment and can be widely used in smart home appliances and corresponding automatic control fields [2,3]. Traditional gesture recognition methods are usually implemented by background filtering and feature extraction. They are easy to be affected by external factors such as light, reduce the performance of the algorithm, and are difficult to obtain satisfactory results
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