Tennis Video Research Articles

The effects of different billboard advertisement language on TV sports audiences’ recognition, recall and search intention of the sponsor signage

PurposeThe impact of various factors on how TV sports audiences perceive sport event sponsors’ billboards around sports facilities has been the subject of extensive research. Despite numerous factors that impact the effectiveness of sponsor signage at sporting events, there has been a lack of research regarding the language used for such signage around sports facilities’ billboards. Therefore, this study aims to investigate the effects of billboard advertisement language on TV sports audiences’ recognition, recall and search intention to sponsor signage.Design/methodology/approachThis study employed an online experimental design. Participants (n = 925) were recruited from two linguistically different regions: Chinese and English. Participants were randomly assigned to one of two conditions: watching tennis video matches with billboard advertisements presented in either the Roman alphabet exclusively or in a combination of the Roman alphabet and Chinese characters.FindingsThis study revealed that although language cannot significantly impact audiences’ unaided recall of a brand, it does have a discernible effect on brand recognition and search intention among audiences. Additionally, people are more likely to search for brands in their native language. Participants from various regions tend to have different recognition rates and search intentions for sport sponsors.Originality/valueThis is the first manuscript examining the use of different languages in relation to audiences’ recognition and recall of sports sponsorship. It provides empirical evidence of the importance of carefully considering the language used in sponsor signage around stadium billboards to optimize the efficacy of sponsorships at sports events.

Tactical Analysis of Table Tennis Video Skills Based on Image Fuzzy Edge Recognition Algorithm

Tactical Analysis of Table Tennis Video Skills Based on Image Fuzzy Edge Recognition Algorithm

Retracted: Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm

Retracted: Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm

Automatic detection method of small target in tennis game video based on deep learning

Due to the large number of frames and low video resolution, tennis match videos cannot accurately identify and extract effective data, which reduces the level of fine analysis of tennis matches. In order to solve the problem of poor detection effect of small targets in tennis video, an automatic detection method of small targets in tennis video based on deep learning is proposed. Non-maximum suppression algorithm is used to determine the position of the target between different adjacent video image sequences, and SVM classifier is used to train a large number of target behaviors. According to the hierarchical structure of dataset annotation, the hierarchical structure of tennis video for deep learning is optimized. The reconstruction algorithm is used to enhance the video image in the input VOC data set and improve the fine segmentation effect of the image. The difference video image is binarized to complete the automatic detection of small targets in tennis video. The experimental results show that the proposed method has high integrity of tennis video information collection, high recognition accuracy and short detection time.

Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm

The application of information technology has realized the transformation of people’s production and lifestyle and also promoted the development of the sports industry. At present, the application of informatization in the field of sports is becoming more and more powerful. By using the advanced methods and technologies of its information display, this paper aimed to realize the research on the tracking of tennis sports video objects in the mobile network environment. It is helpful to analyze and solve the objectivity problems such as most of the loopholes in the single research and traditional methods of tennis video target tracking research. By drawing on the principles and rules of machine learning algorithms, the tennis video target tracking research is carried out, and the informatization and dataization of tennis are realized. In the experiment of the target tracking algorithm, 12 tracking videos have higher tracking accuracy than other parameters. The overall tracking accuracy of the video sequence under grayscale feature was 0.694; the tracking accuracy was the highest, but the tracking speed was the lowest compared to other parameters in the experiment. Therefore, it is very important to study the target tracking of tennis sports in the tennis field.

Table Tennis Capture System Based on Image Recognition and Modeling

This article takes table tennis as the research object, mainly extracts a large number of table tennis video trajectories, and combines the kinematic analysis method to reduce the noise of the extracted target to improve the accuracy of the table tennis trajectory and obtain the table tennis trajectory. The parameter group is used to simulate the trajectory of table tennis; based on the MATLAB environment to realize the table tennis trajectory simulation that provides the initial velocity and coordinates, complete the capture of the table tennis drop point. This article uses the two-dimensional information in the image to estimate the parameter values that form the three-dimensional information and then calculates the three-dimensional information based on the estimated parameter values. The unscented Kalman filter is used to estimate the trajectory parameters and rotation parameters of table tennis, and the algorithm for calculating and updating markers in real time is proposed, which reduces the influence of calculation errors on the estimation process and improves the accuracy of parameter estimation. First, establish a table tennis movement model and choose a suitable model to describe the translation and rotation movement of the table tennis. Then, based on the extended Kalman filter algorithm and the unscented Kalman filter algorithm, the ball flight trajectory estimation algorithm is established. Finally, an actual image acquisition system is built to collect trajectory information and surface information images from the actual table tennis movement. Processing is performed to obtain the positions of the center of the ping pong ball and the marked points on the corresponding sensors. Simulation and experimental results show that the proposed algorithm can effectively estimate the trajectory, linear velocity, and angular velocity parameters of a table tennis ball, and the selection of the initial value has little effect on the algorithm.

Object Extraction of Tennis Video Based on Deep Learning

Moving target detection and tracking technology is the core technology in the field of computer vision. It integrates image processing, pattern recognition and intelligence, and artificial intelligence and automatic control are the keys to an intelligent video surveillance system. The method acquires video image signals through visible light or infrared sensors, performs digital image processing on the video images, detects moving targets, and then extracts moving targets for target recognition. Then, the moving target is predicted and tracked according to the image features and spatiotemporal features, and the contour shape, position, and motion trajectory of the target are obtained, which provides data support for subsequent tasks. This paper uses the convolutional neural network model HyperNet as the technical support to study the deep learning (DL) tennis video target extraction. The final experimental results show that the loss value of the loss function in the training process of this method is stably maintained between 1.5% and 2.5%, and the speed performance is also greatly improved. The number of boxes for extracting candidate regions is significantly reduced, the calculation time for each frame will not exceed 1.8 s, the orientation accuracy of target extraction is 96.32%, and the size accuracy is 91.05%.

Augmenting Sports Videos with VisCommentator.

Visualizing data in sports videos is gaining traction in sports analytics, given its ability to communicate insights and explicate player strategies engagingly. However, augmenting sports videos with such data visualizations is challenging, especially for sports analysts, as it requires considerable expertise in video editing. To ease the creation process, we present a design space that characterizes augmented sports videos at an element-level (what the constituents are) and clip-level (how those constituents are organized). We do so by systematically reviewing 233 examples of augmented sports videos collected from TV channels, teams, and leagues. The design space guides selection of data insights and visualizations for various purposes. Informed by the design space and close collaboration with domain experts, we design VisCommentator, a fast prototyping tool, to eases the creation of augmented table tennis videos by leveraging machine learning-based data extractors and design space-based visualization recommendations. With VisCommentator, sports analysts can create an augmented video by selecting the data to visualize instead of manually drawing the graphical marks. Our system can be generalized to other racket sports (e.g., tennis, badminton) once the underlying datasets and models are available. A user study with seven domain experts shows high satisfaction with our system, confirms that the participants can reproduce augmented sports videos in a short period, and provides insightful implications into future improvements and opportunities.

Multi-Target Detection of Table Tennis Video Based on CNN and Yolov3

In order to explore the effect of convolutional neural network (CNN) on the detection of athletes and balls in table tennis, and to solve the problems of low accuracy and weak generalization ability of table tennis athletes training data, this paper analyzed videos of table tennis athletes. For the detection of multiple targets in the videos, including athletes and balls, we used Yolov3 as a deep learning framework, and CNN as an automatic detection method when processing images. We trained and test the video data to improve the stability and accuracy of target detection, through modifying its model on the basis of the Yolov3 model. Finally, we detect the movement trajectories of athletes and balls in table tennis videos stably, and the accuracy is above 0.8.

Ice hockey spectators use contextual cues to guide predictive eye movements

Eye movements are an integral part of human visual perception. They allow us to have a small foveal region with exquisite acuity and at the same time a large visual field. For a long time, eye movements were regarded as machine-like behaviors in response to visual stimulation1, but over the past few decades it has been convincingly shown that expectations, intended actions, rewards and many other cognitive factors can have profound effects on the way we move our eyes2-4. In order to be useful, our oculomotor system must minimize delay with respect to the dynamic events in the visual scene. The ability to do so has been demonstrated in situations where we are in control of these events, for example when we are making a sandwich or tea5, and when we are active participants, for example when hitting a cricket ball6. But what about scenes with complex dynamics that we do not control or directly take part in, like a hockey game we are watching as a spectator? A semantic influence on gaze fixation location during viewing of tennis videos has been suggested before7. Here we use carefully annotated hockey videos to show that the brain is indeed able to exploit the semantic context of the game to anticipate the continuous motion of the puck, leading to eye movements that are fundamentally different than when following exactly the same motion without any context.

Taylor rate-distortion trade-off and adaptive block search for HEVC encoding

The advancement in high-efficiency video coding (HEVC) is adapted for defining the subsequent generation compression model to offer efficient compression without affecting the image quality. The HEVC offers improved performance than the existing compression models. Accordingly, this work develops an approach for video compression by proposing weighted entropy coding and adaptive block search-based rate-distortion (R-D) trade-off, named Taylor R-D trade-off. The adaptive block search R-D trade-off, by integrating the hexagon-based tree search algorithm (HBTSA), along with the Taylor R-D trade-off for initiating the block search process of motion estimation in video coding, which selects the optimal block. Initially, the frames are extorted from the input video. Then, the video frames are divided into macroblocks to perform the adaptive block search. Further, the suitable blocks are selected and given to the encoding process by weighted context-adaptive binary arithmetic coding (CABAC) that employs a weighted entropy function to persist the video quality after the compression. The results analysis shows that the proposed HBTSA method has improved PSNR and SSIM values of 42.717dB, and 0.991, respectively, using football, coast guard, garden, and tennis videos.

Classification of Tennis Video Types Based on Machine Learning Technology

With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content‐based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color‐coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large‐scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.

Improvement of table tennis technology based on data mining in the environment of wireless sensor networks

With the development of Internet of things technology, the combination of Internet of things technology and sports competition parameter collection technology, so as to carry out rapid and accurate retrieval and positioning of technology and tactics, has innovation in the current research field. In the high-level table tennis competition, the use of technology and tactics is closely related to the gain and loss of points. At present, the traditional table tennis video mining algorithm has some problems such as low efficiency and poor performance of optimization classification. Based on this, this article introduces the big data platform of the wireless sensor networks to construct the table tennis match database, realizing the real-time updating of table tennis match parameters and the call of historical data at any time. Then establishing a data mining model to realize the data and dynamic analysis of table tennis matches. Finally, based on this strategic analysis system, the data collected from two table tennis competitions are simulated, and the tactical recommendation of theoretical analysis is obtained, which provides a theoretical basis for the digitization of table tennis sports.

Gesture recognition for human–machine interaction in table tennis video based on deep semantic understanding

The analysis of moving objects in videos, especially the recognition of human motions and gestures, is attracting increasing emphasis in computer vision area. However, most existing video analysis methods do not take into account the effect of video semantic information. The topological information of the video image plays an important role in describing the association relationship of the image content, which will help to improve the discriminability of the video feature expression. Based on the above considerations, we propose a video semantic feature learning method that integrates image topological sparse coding with dynamic time warping algorithm to improve the gesture recognition in videos. This method divides video feature learning into two phases: semi-supervised video image feature learning and supervised optimization of video sequence features. Next, a distance weighting based dynamic time warping algorithm and K-nearest neighbor algorithm is leveraged to recognize gestures. We conduct comparative experiments on table tennis video dataset. The experimental results show that the proposed method is more discriminative to the expression of video features and can effectively improve the recognition rate of gestures in sports video.

Information Accrual From the Period Preceding Racket-Ball Contact for Tennis Ground Strokes: Inferences From Stochastic Masking.

Previous research suggests the existence of an expert anticipatory advantage, whereby skilled sportspeople are able to predict an upcoming action by utilizing cues contained in their opponent’s body kinematics. This ability is often inferred from “occlusion” experiments: information is systematically removed from first-person videos of an opponent, for example, by stopping a tennis video at the point of racket-ball contact, yet performance, such as discrimination of shot direction, remains above chance. In this study, we assessed the expert anticipatory advantage for tennis ground strokes via a modified approach, known as “bubbles,” in which information is randomly removed from videos in each trial. The bubbles profile is then weighted by trial outcome (i.e., a correct vs. incorrect discrimination) and combined across trials into a classification array, revealing the potential cues informing the decision. In two experiments (both with N = 34 skilled tennis players) we utilized either temporal or spatial bubbles, applying them to videos running from 0.8 to 0 s before the point of racket-ball contact (cf. Jalali et al., 2018). Results from the spatial experiment were somewhat suggestive of accrual from the torso region of the body, but were not compelling. Results from the temporal experiment, on the other hand, were clear: information was accrued mainly during the period immediately prior to racket-ball contact. This result is broadly consistent with prior work using nonstochastic approaches to video manipulation, and cannot be an artifact of temporal smear from information accrued after racket-ball contact, because no such information was present.

A Real-Time Court Detection and Tracking System to Tennis Videos

Abstract To analyze a tennis video sequence at a higher semantic level, the first step is to determine the position of the court. Thus, it is required to track the court in the scene. In this article, we proposed a new automatic system for tennis court detection and tracking in real time. Our system comprises a structural analysis system that extracted the court view images to be processed by classifying the tennis video to non-court view or court view frame. Then, the initialization step is dedicated to detecting the court in the first frame. It consists of extracting the tennis court line using the Probabilistic Hough Transform and estimating the homography between the tennis court model and the detected court lines. To ensure the accuracy of the obtained homography, it should pass a rejection test. We used the camera motion as a supplementary information, and we tracked the court only in case where the camera is moving, which improves the stability of our system. In addition, for each court view frame, we updated the homography, and we fitted the projected model based on the algorithm of Levenberg–Marquardt.

A new model based approach for tennis court tracking in real time

The detection and the tracking of the tennis court is a primordial step to analyse a tennis video at higher semantic level. In this context, a new approach for tennis court tracking in real time is proposed in this paper. Our proposed system is based on model based approach allows to compute the homography between the court detected in the scene and the court model presenting the real world coordinate. For this aim, the first step is to detect the tennis court by detecting the court line and determining some interest points. We check then the motion of the camera. In case of camera motion, the court is tracked by tracking the interest points using the Lucas-Kanade algorithm. After that, these points are used by a RANSAC algorithm to estimate the homography. However, in case of a fixed camera, we need only the model based correction system.

Teachers prevent fatal AI mistakes

Artificial intelligence needs people's help. The best AIs are quickly mastering skills from lip-reading to playing video games, but only by learning through repeated failure. As robots take on domains like healthcare and driving, where mistakes can kill, this is no longer an acceptable approach. Fortunately, a new study suggests that with the right human oversight, it might be possible to ditch the failures. To try to train an AI without it making a mistake, Owain Evans at the University of Oxford and his colleagues started with the simple two-dimensional table tennis video game Pong.

THE DIFFERENCES OF INTRUCTIONAL MEDIA AND COORDINATION IN LEARNING OUTCOMES OF GROUNDSTROKES TENNIS ON NOVICE LEVEL ATHLETES

AbstractThe purpose of this study was to find out the differences in instructional media and coordination to learning outcomes on tennis groundstrokes of novice athletes. This experimental research with 2 x 3 of factorial design was conducted in the Bengkulu city, Data was collected through groundstrokes tennis test. The analysis of data outcomes was stated (1) demonstration and video tennis training media gave different learning outcomes of groundstrokes tennis on students. (2) demonstration and tennis video game media gave different learning outcomes of groundstrokes tennis on students. (3) video tennis training and tennis video game media gave different learning outcomes of groundstrokes tennis on students. (4) there were differences in instructional media and coordination skill on learning outcomes of groundstrokes tennis on students. (5) demonstration and video tennis training media gave different learning outcomes of groundstrokes tennis on students with high coordination skill. (6) demonstration and tennis video game media gave different learning outcomes of groundstrokes tennis on students with high coordination skill. (7) no learning outcomes differences of groundstrokes tennis between video tennis training and tennis video game media on students with high coordination skill. (8) no learning outcomes differences of groundstrokes tennis between demonstration and video tennis training media on students with low coordination skill. (9) no learning outcomes differences of groundstrokes tennis between demonstration and tennis video game media on students with low coordination skill. (10) video tennis training and tennis video game media gave different learning outcomes of groundstrokes tennis on students with low coordination skill.The results were recommended that video tennis training media should be used to train the skill of groundstrokes tennis on student with low coordination skill, while demonstration media should be used for novices with high coordination skill.Keywords : Instructional Media, Coordination, Groundstrokes.

Analysis of Slow Motion Based on Adaptive Detection Method in Tennis Video

Analysis of Slow Motion Based on Adaptive Detection Method in Tennis Video