Improving dynamic gesture recognition in untrimmed videos by an online lightweight framework and a new gesture dataset ZJUGesture

Human–computer interaction technology brings great convenience to people, and dynamic gesture recognition makes it possible for a man to interact naturally with a machine. However, recognizing gestures quickly and precisely in untrimmed videos remains a challenge in real-world systems since: (1) It is challenging to locate the temporal boundaries of performing gestures; (2) There are significant differences in performing gestures among different people, resulting in a variety of gestures; (3) There must be a trade-off between the accuracy and the computational consumption. In this work, we propose an online lightweight two-stage framework, including a detection module and a gesture recognition module, to precisely detect and classify dynamic gestures in untrimmed videos. Specifically, we first design a low-power detection module to locate gestures in time series, then a temporal relational reasoning module is employed for gesture recognition. Moreover, we present a new dynamic gesture dataset named ZJUGesture, which contains nine classes of common gestures in various scenarios. Extensive experiments on the proposed ZJUGesture and 20-bn-Jester dataset demonstrate the attractive performance of our method with high accuracy and a low computational cost.