Motion Feature Retrieval in Basketball Match Video Based on Multisource Motion Feature Fusion

Both the human body and its motion are three-dimensional information, while the traditional feature description method of two-person interaction based on RGB video has a low degree of discrimination due to the lack of depth information. According to the respective advantages and complementary characteristics of RGB video and depth video, a retrieval algorithm based on multisource motion feature fusion is proposed. Firstly, the algorithm uses the combination of spatiotemporal interest points and word bag model to represent the features of RGB video. Then, the directional gradient histogram is used to represent the feature of the depth video frame. The statistical features of key frames are introduced to represent the histogram features of depth video. Finally, the multifeature image fusion algorithm is used to fuse the two video features. The experimental results show that multisource feature fusion can greatly improve the retrieval accuracy of motion features.

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.

Using Computational Analysis of Behavior To Discover Developmental Change In Memory-Guided Attention Mechanisms In Childhood

We tested 4-9.5-year-old children on a naturalistic memory-guided attention visual search task. We measured fixation distribution during a search using wearable eye tracking, and simultaneously recorded depth video data for each participant and used computer vision algorithms to track them during navigation. We manipulated object placement and trial order such that nearby objects would be encountered during initial search for reference objects. We used a computational model of top-down guidance for reference object visual features and examined the use of this top-down attention for reference objects during subsequent nearby object search. The data suggest that the value of physical navigation during initial spatial exploration for subsequent memory-guided attention, specifically in early childhood, is in its association with stronger visual representations of goal reference objects during spatial exploration. By middle childhood, visual search times were not impacted by memory engagement.

Boundary-crossers: How providers facilitate ethnic minority families’ access to dementia services

Background Providers who work closely with ethnic minority people with dementia and their families are pivotal in helping them access services. However, few studies have examined how these providers actually do this work. Using the concept of ‘boundary crossers,’ this article investigates the strategies applied by these providers to facilitate access to dementia services for ethnic minority people with dementia and their families. Methods Between 2017 and 2020, in-depth video-recorded interviews were conducted with 27 health, aged care, and community service providers working with ethnic minority people living with dementia across Australia. Interviews were conducted in language and in English, then translated and transcribed verbatim. The data were analyzed thematically. Results Family and community stigma associated with dementia and extra-familial care were significant barriers to families engaging with services. To overcome these barriers, participants worked at the boundaries of culture and dementia, community and systems, strategically using English and other vernaculars, clinical and cultural terminology, building trust and rapport, and assisting with service navigation to improve access. Concurrently, they were cognizant of familial boundaries and were careful to provide services that were culturally appropriate without supplanting the families’ role. Conclusions In negotiating cultural, social, and professional boundaries, providers undertake multidimensional and complex work that involves education, advocacy, negotiation, navigation, creativity, and emotional engagement. This work is largely under-valued but offers a model of care that facilitates social and community development as well as service integration across health, aged care, and social services.