Biography

I am pursuing my Master’s degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include action recognition and temporal action proposals in videos.

Research and Interests

• Action Recognition

Publications

• Chao Xu, Xia Wu, Mengmeng Wang, Feng Qiu, Yong Liu, and Jun Ren. Improving Dynamic Gesture Recognition in Untrimmed Videos by An Online Lightweight Framework and A New Gesture Dataset ZJUGesture. Neurocomputing, 523:58-68, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  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.

  @article{xv2022idg,
  title = {Improving Dynamic Gesture Recognition in Untrimmed Videos by An Online Lightweight Framework and A New Gesture Dataset ZJUGesture},
  author = {Chao Xu and Xia Wu and Mengmeng Wang and Feng Qiu and Yong Liu and Jun Ren},
  year = 2023,
  journal = {Neurocomputing},
  volume = 523,
  pages = {58-68},
  doi = {10.1016/j.neucom.2022.12.022},
  abstract = {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.}
  }