Research on Care Workers Physical Exercise Video Tracking based on Deep Neural Network

Most nursing workers have a positive cognitive attitude towards physical exercise, but their exercise behavior lags behind. There are significant differences in the frequency, time, experience and load of physical exercise among nurses of different ages. Care workers’ union organizations should try their best to provide health assistance to medical staff to meet their needs for physical exercise, so as to ensure their physical and mental health. Aiming at the problem of target tracking in motion video, this paper proposes a method of tracking motion video of nursing staff based on deep neural network (DNN). The effectiveness and adaptability of this method are verified by an example. This method can not only track and estimate the position of the target effectively, but also describe the shape of the target well, thus solving the problem that the shape of the target is complex and difficult to track in motion video.

Improving the Students’ Speaking Ability of Report Text through Stop Motion Video Technique in Junior High School

This research was Classroom Action Research. This research aimed at implementing stop motion video technique to improve students speaking ability of report text in junior high school and the students’ perception in implementation of stop motion video technique to improve the students’ speaking ability of report text. The subject of this research was the second grade of junior high school. The focus of this research was the students’ speking ability. The data were in the form of students’ speaking score. The data were obtained through conducting teaching learning process. The result of this research was that there were improvement in students’ speaking ability through Stop Motion Video technique in whole skills. The improvement was up to 40%-90%. They were pronunciation, grammar, vocabulary, fluency, and understanding. The students’ perception showed that all of the students were motivated after conducted this learning technique. This was showed by the questionnaires filled by the students.

Raccontare con le immagini. Lo Stop-motion come strumento per l’apprendimento collaborativo

The purpose of this paper is to describe the activities carried out in a class with children of the third year of primary school (Istituto Sant’Anna, Torino) school year 2019/2020 and to test the potentialities of audiovisual and technological language in the development of knowledge and skills, like the management of a collaborative activity, the correct use of technological tools and the awarness of the project’s path.Thissperimental activity starts from the hypothesis that creating a stop-motion video, focused on a disciplinary subject, can give good results both from a learning point of view and the acquisition of social and relational skills thank to the type work, mainly carried out in groups.

Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling

Traditional aerobics training methods have the problems of lack of auxiliary teaching conditions and low-training efficiency. With the in-depth application of artificial intelligence and computer-aided training methods in the field of aerobics teaching and practice, this paper proposes a local space-time preserving Fisher vector (FV) coding method and monocular motion video automatic scoring technology. Firstly, the gradient direction histogram and optical flow histogram are extracted to describe the motion posture and motion characteristics of the human body in motion video. After normalization and data dimensionality reduction based on the principal component analysis, the human motion feature vector with discrimination ability is obtained. Then, the spatiotemporal pyramid method is used to embed spatiotemporal features in FV coding to improve the ability to identify the correctness and coordination of human behavior. Finally, the linear model of different action classifications is established to determine the action score. In the key frame extraction experiment of the aerobics action video, the ST-FMP model improves the recognition accuracy of uncertain human parts in the flexible hybrid joint human model by about 15 percentage points, and the key frame extraction accuracy reaches 81%, which is better than the traditional algorithm. This algorithm is not only sensitive to human motion characteristics and human posture but also suitable for sports video annotation evaluation, which has a certain reference significance for improving the level of aerobics training.

Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception

In the special sports camera, there are subframes. A lens is composed of multiple frames. It will be unclear if a frame is cut out. The definition of video screenshots lies in the quality of video. To get clear screenshots, we need to find clear video. The purpose of this paper is to analyze and evaluate the quality of sports video images. Through the semantic analysis and program design of video using computer language, the video images are matched with the data model constructed by research, and the real-time analysis of sports video images is formed, so as to achieve the real-time analysis effect of sports techniques and tactics. In view of the defects of rough image segmentation and high spatial distortion rate in current sports video image evaluation methods, this paper proposes a sports video image evaluation method based on BP neural network perception. The results show that the optimized algorithm can overcome the slow convergence of weights of traditional algorithm and the oscillation in error convergence of variable step size algorithm. The optimized algorithm will significantly reduce the learning error of neural network and the overall error of network quality classification and greatly improve the accuracy of evaluation. Sanda motion video image quality evaluation method based on BP (back propagation) neural network perception has high spatial accuracy, good noise iteration performance, and low spatial distortion rate, so it can accurately evaluate Sanda motion video image quality.

Examining a display-peeping prevention method that uses real-time UI part transparency linked to motion detection by video analysis

In recent years, the use of various information terminals such as smartphones and personal computers have become widespread, and situations where information terminals are used have become diverse. With increased opportunities to use information terminals outdoors and during travel, some users have been using peep-prevention filters, or software with an equivalent function, on their displays, in order to protect their privacy. However, such filters have problems with regards their effectiveness, ease of use, and the user being able recognize when they are vulnerable to peeping. Decrease in display visibility, unprotected angles, and the fact that it is difficult for users to notice when others are watching their screen, are some examples of such problems. Also, recently, many information terminals recently distributed have built-in cameras. In this paper, in order to solve the aforementioned problems, we propose to detect motion, video analyze , and transparentize part of the user interface (UI) in real time by using a laptop’s built-in camera. This method is enabled with low-load and can be applied to various terminals. Further, in order to verify the effectiveness of the method, we implemented a prototype, and carried out an evaluation experiment on experimental subjects. Results from the experiment confirmed that real-time UI transparentization is a very effective method for protecting privacy of information terminals.

EXPRESS: The Effect of Slow Motion Video on Consumer Inference

Video advertisements often show actors and influence agents consuming and enjoying products in slow motion. By prolonging depictions of influence agents’ consumption utility, slow motion cinematographic effects ostensibly enhance social proof and signal product qualities that are otherwise difficult to infer visually (e.g., pleasant tastes, smells, haptic sensations, etc.). Seven studies including an eye-tracking study, a Facebook Ads field experiment, and lab and online experiments—all using real ads across diverse contexts—demonstrate that slow motion (vs. natural speed) can backfire and undercut product appeal by making the influence agent’s behavior seem more intentional and extrinsically motivated. The authors rule out several alternative explanations by showing that the effect attenuates for individuals with lower intentionality bias, is mitigated under cognitive load, and reverses when ads use non-human influence agents. The authors conclude by highlighting the potential for cross-pollination between visual information processing and social cognition research, particularly in contexts such as persuasion and trust, and discuss managerial implications for visual marketing, especially on digital and social platforms.