%0 Journal Article %A LIU Jia %A BIAN Fangzhou %A CHEN Dapeng %A LI Weibin %T Fingertip Detection Model Based on UGF-Net %D 2022 %R 10.3778/j.issn.1002-8331.2010-0235 %J Computer Engineering and Applications %P 225-231 %V 58 %N 5 %X In the field of human-computer interaction, accurate fingertip detection has a great impact on the richness and flexibility of interaction. However, due to the small size of fingertip, accurate and robust fingertip detection is still a challenging task. In order to improve the accuracy of fingertip detection, this paper proposes a fingertip detection model based on depth convolution neural network UGF-Net(unified-gesture-and-fingertip- network). The model can be used for fingertip detection and gesture recognition at the same time. The YOLO algorithm is used to extract the gesture area, and the FCNN output visual Gaussian heat map is used to realize fingertip detection. Finally, the effectiveness and robustness of the proposed fingertip detection model are verified by experiments. The model is tested on the SCUT-Ego-Gesture data set. The results show that the accuracy of fingertip detection can reach 99.8%, and the average frame rate of real-time video image is 34.5 frame/s, which meets the requirements of real-time. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2010-0235