Application of Convolutional Neural Network in Dynamic Gesture Tracking

Abstract

In order to solve the tracking of dynamic gesture targets in complicated scenarios, an improved YOLOv3 (you only look once) real-time tracking algorithm is put forward for dynamic gestures. First, concerning the problem of poor real-timeliness of YOLOv3 on-line inspection, the main network structure of YOLOv3 is improved by using the characteristics of single target inspection of gestures. Second, an inspection tracking method suitable to the planned area of gesture tracking under complicated scenarios is put forward to inspect gesture targets, screen impacts of targets not being tracked at the moment in the background and complete real-time tracking of gestures. Last, training and testing are conducted in a unified manner to designed gesture data. Experimental results show that, for gesture tracking under complicated scenarios, the algorithm outperforms YOLOv3 algorithm and related target tracking algorithms.

Keywords

• dynamic gestures
• yolo (you only look once)
• real-time tracking
• convolutional neural network (cnn)