Research on Athlete Behavior Recognition Technology in Sports Teaching Video Based on Deep Neural Network

In recent years, due to the simple design idea and good recognition effect, deep learning method has attracted more and more researchers’ attention in computer vision tasks. Aiming at the problem of athlete behavior recognition in mass sports teaching video, this paper takes depth video as the research object and cuts the frame sequence as the input of depth neural network model, inspired by the successful application of depth neural network based on two-dimensional convolution in image detection and recognition. A depth neural network based on three-dimensional convolution is constructed to automatically learn the temporal and spatial characteristics of athletes’ behavior. The training results on UTKinect-Action3D and MSR-Action3D public datasets show that the algorithm can correctly detect athletes’ behaviors and actions and show stronger recognition ability to the algorithm compared with the images without clipping frames, which effectively improves the recognition effect of physical education teaching videos.

Designing Curricular Games

Most teachers only dream of their students spending the amount of motivation, attention, passion, and critical thinking on their classes that some do playing video games. Many teachers have thought “How can I compete with that?” as they confiscate a hand-held gaming device from a student. However, more and more teachers are incorporating video games into their curriculum, instead of banning them. This chapter argues that it is not just video games that can transform teaching, video game techniques can as well. By using video game design principles such as game stories and quests, teachers can restructure their teaching so students do not just learn the curriculum, they experience it. This chapter explores the research on using game-based teaching and learning with the Next Generation including how game-based teaching can be used to achieve 21st century goals as outlined by business leaders within 21st century educational constraints.

Spatio-temporal Attention Network for Student Action Recognition in Classroom Teaching Videos

Inspired by the wide application of transformer in computer vision and its excellent ability in temporal feature learning. This paper proposes a novel and efficient spatio-temporal residual attention network for student action recognition in classroom teaching video. It first fuses 2D spatial convolution and 1D temporal convolution to study spatio-temporal feature, then combines the powerful Reformer to better study the deeper spatio-temporal characteristics with visual significance of student classroom action. Based on the spatio-temporal residual attention network, a single person action recognition model in classroom teaching video is proposed. Considering that there are often multiple students in the classroom video scene, on the basis of single person action recognition, combined with object detection and tracking technology, the association of temporal and spatial characteristics of the same student targets is established, so as to realize the multi-student action recognition in classroom video scene. The experimental results on classroom teaching video dataset and public video dataset show that the proposed model achieves higher action recognition performance than the existing excellent models and methods.

Visual communication in research: a third space between science and art

Ample scientific literature recognises the role of visual thinking in the constructive process of ideas and mental images and the function of visual intelligence in the communicative processes. Starting from the sectoral studies, we have turned our attention to the visual communication of the results of scientific research, relating it to some characteristics of artistic communication to find a shared ground, that is, a third space inhabited by common languages and competencies. In so doing, we have overcome the traditional antinomy between humanists and scientists, starting instead from the results of a recent study that has shown how such an opposition does not find real confirmation in the sector of science communication. We have thus analysed three case studies (graphical abstract, augmented reality, audiovisual documentation) on the grounds of a 10-year long experience of research in the field of visual communication (iconography and iconology, art teaching, video research) to acknowledge visual thinking and graphical/artistic competencies, situated in the third space between didactics and art, a fundamental role in the formation of a scientist and a researcher.