Basketball Video Research Articles

CNN-LSTM Hybrid Model for Kinematic Feature Analysis and Parabolic Radian Prediction in Basketball Videos.

With the improvement of living standards around the world, people's love for sports has also increased; basketball is especially loved by people. It is of great importance to provide sound motor instruction for basketball. To this end, this paper comprehensively investigates the dependence between the optimal release conditions and the corresponding shooting arm movements in basketball players. We carry out kinematic feature analysis of basketball sports videos, propose a hybrid CNN-LSTM model that can predict the arc of the shooting parry, and identify the key movements of the arm joint that produce optimal release velocity, angle, and backspin in short-, mid-, and long-range shots. The experiment demonstrates that the model has three rigid planar links with rotational joints that mimic the shoulder, elbow, and wrist joints of the upper arm, forearm, and hand, which are better at guiding the optimal ball release speed, angle, and backspin for different players with the fastest ball speed being about 4.6 m/s and the slowest being about 1.7 m/s.

Fusing motion patterns and key visual information for semantic event recognition in basketball videos

Many semantic events in team sport activities e.g. basketball often involve both group activities and the outcome (score or not). Motion patterns can be an effective means to identify different activities. Global and local motions have their respective emphasis on different activities, which are difficult to capture from the optical flow due to the mixture of global and local motions. Hence it calls for a more effective way to separate the global and local motions. When it comes to the specific case for basketball game analysis, the successful score for each round can be reliably detected by the appearance variation around the basket. Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos. Firstly, an algorithm is proposed to estimate the global motions from the mixed motions based on the intrinsic property of camera adjustments. And the local motions could be obtained from the mixed and global motions. Secondly, a two-stream 3D CNN framework is utilized for group activity recognition over the separated global and local motion patterns. Thirdly, the basket is detected and its appearance features are extracted through a CNN structure. The features are utilized to predict the success or failure. Finally, the group activity recognition and success/failure prediction results are integrated using the kronecker product for event recognition. Experiments on NCAA dataset demonstrate that the proposed method obtains state-of-the-art performance.

USA: Public Display of Player’s Tattoo in Basketball Video Game as Fair Use

USA: Public Display of Player’s Tattoo in Basketball Video Game as Fair Use

Active Player Detection in Handball Scenes Based on Activity Measures.

In team sports training scenes, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the most active player who performs the given handball technique. This is a very challenging task, for which, apart from an accurate object detector, which is able to deal with complex cluttered scenes, additional information is needed to determine the active player. We propose an active player detection method that combines the Yolo object detector, activity measures, and tracking methods to detect and track active players in time. Different ways of computing player activity were considered and three activity measures are proposed based on optical flow, spatiotemporal interest points, and convolutional neural networks. For tracking, we consider the use of the Hungarian assignment algorithm and the more complex Deep SORT tracker that uses additional visual appearance features to assist the assignment process. We have proposed the evaluation measure to evaluate the performance of the proposed active player detection method. The method is successfully tested on a custom handball video dataset that was acquired in the wild and on basketball video sequences. The results are commented on and some of the typical cases and issues are shown.

Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm

Sports analysis has recently attracted increasing research efforts in computer vision. Among them, basketball video analysis is very challenging due to severe occlusions and fast motions. As a typical tracking-by-detection method, k-shortest paths (KSP) tracking framework has been well used for multiple-person tracking. While effective and fast, the neglect of the appearance model would easily lead to identity switches, especially when two or more players are intertwined with each other. This paper addresses this problem by taking the appearance features into account based on the KSP framework. Furthermore, we also introduce a similarity measurement method that can fuse multiple appearance features together. In this paper, we select jersey color and jersey number as two example features. Experiments indicate that about 70% of jersey color and 50% of jersey number over a whole sequence would ensure our proposed method preserve the player identity better than the existing KSP tracking method.

Analysis of technical features in basketball video based on deep learning algorithm

The research of video-based sports movement analysis technology has an important application value. The introduction of digital video, human–computer interaction and other technologies in sports training can greatly improve training efficiency. This paper studies the technical characteristics of the players in the basketball game video and proposes a behavior analysis method based on deep learning. We first design a method to automatically extract the basketball court and stadium marking line. Subsequently, key frames in the video are captured using a spatiotemporal scoring mechanism. Afterward, we develop a behavior recognition and prediction method based on an encoder–decoder framework. The analysis results can be fed back to coaches and data analysts in real-time to help them analyze the tactics and technical choices. Experiments on the proposed method are carried out on a large basketball video dataset. The results show that the proposed method can effectively identify the motion of video characters while achieving high behavior analysis accuracy.

Quality Evaluation of Degraded Basketball Video Image Restoration based on Classification Learning

Quality Evaluation of Degraded Basketball Video Image Restoration based on Classification Learning

Video analysis method of basketball training assistant based on deep learning theory during COVID-19 spread

During covid-19, basketball training was stopped. Instead, the basketball video analysis is used. In this paper, literature, theoretical analysis, numerical simulation, experimental research and other research methods are used. The ant colony algorithm model of deep learning optimization for basketball technical and tactical decision-making is established to solve the optimization problem of actual technical and tactical decision-making. In this paper, video image correlation algorithm is used. In the video of players’ free throw basket, there are many independent frames. The real frame set of free throw basket includes the whole process of jumping, arm lifting, squatting and stretching. The shooting frame set and shooting information of the ball are obtained. In this paper, a shot frame detection algorithm is proposed by analyzing multiple samples of multi shot video. The mathematical model of the shooting frame is established, which can locate the shooting frame quickly and accurately and determine the penalty frame set. Further obtain the basketball release status information for preparation. The reliability and robustness of the algorithm are verified by experiments on several samples. It provides a new method for basketball training during covid-19.

WITHDRAWN: Video structure syntax mining based on Hidden Markov Model in sports video

Abstract On the basis of shot segmentation, an improved Rate Sensitive Competitive Learning algorithm (RSCLA) is proposed for unsupervised shot clustering based on NBA basketball video, which converts video stream data into symbol sequence. Aiming at the characteristics of sequence correlation, time correlation and no clear transaction concept in Video Association rules, this paper improved the traditional Apriori algorithm and proposed a video Association Rules Mining Algorithm based on time-based window computing support. The periodic or semi-periodic structural grammar patterns in video are explored with frequent sets of association rules to guide the establishment of video structure semantic mining model. Aiming at the limitation of string matching, this paper proposes a HMM-based pattern mining method to analyze high-level video events, identify and locate free throw events in basketball videos. The experimental results show that the four-state HMM is the inner free throw mode of basketball video and achieves good performance.

Experimental evaluation of matching via a commercially available basketball video game.

Many recent nonlaboratory-based quantitative analyses of behavior have relied on archival competitive sporting data. However, the ratio-based reinforcement schedules in most athletic competitions and the correlational nature of archival data analyses raise concern over the contributions of such findings to the behavior analytic literature. The current experiment evaluated whether matching in a human operant paradigm would approximate matching observed in nonlaboratory-based sports data. To this end, we used in-game attributes to parametrically manipulate 2- and 3-point shooting in a commercially available basketball video game. The behavior of 6 of 9 participants conformed to the generalized matching equation. These results suggest matching in sporting contexts may be a product of restricted nonindependent concurrent random-ratio schedules. Implications of this experiment, including those in applied behavior analysis and potential influence on gamification, are discussed.

Ontology Based Global and Collective Motion Patterns for Event Classification in Basketball Videos

In multi-person videos, especially team sport videos, a semantic event is usually represented as a confrontation between two teams of players, which can be represented as collective motion. In broadcast basketball videos, specific camera motions are used to present specific events. Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion. A semantic event in basketball videos can be generally divided into three stages: pre-event, event occurrence (event-occ), and post-event. In this paper, we propose an ontology-based global and collective motion pattern (On_GCMP) algorithm for basketball event classification. First, a two-stage GCMP based event classification scheme is proposed. The GCMP is extracted using optical flow. The two-stage scheme progressively combines a five-class event classification algorithm on event-occs and a two-class event classification algorithm on pre-events. Both algorithms utilize sequential convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to extract the spatial and temporal features of GCMP for event classification. Second, we utilize post-event segments to predict success/failure using deep features of images in the video frames (RGB_DF_VF) based algorithms. Finally the event classification results and success/failure classification results are integrated to obtain the final results. To evaluate the proposed scheme, we collected a new dataset called NCAA+, which is automatically obtained from the NCAA dataset by extending the fixed length of video clips forward and backward of the corresponding semantic events. The experimental results demonstrate that the proposed scheme achieves the mean average precision of 58.10% on NCAA+. It is higher by 6.50% than state-of-the-art on NCAA.

Player trajectory reconstruction for tactical analysis

To increase the performance of sport team, the tactical analysis of team from game video is essential. Trajectories of the players are the most useful cues in a sport video for tactical analysis. In this paper, we propose a technique to reconstruct the trajectories of players from broadcast basketball videos. We first propose a mosaic based approach to detect the boundary lines of court. Then, the locations of players are determined by the integration of shape and color visual information. A layered graph is constructed for the detected players, which includes all possible trajectories. A dynamic programming based algorithm is applied to find the trajectory of each player. Finally, the trajectories of players are displayed on a standard basketball court model by a homography transformation. In contrast to related works, our approach exploits more spatio-temporal information in video. Experimental results show that the proposed approach works well and outperforms some existing technique.

Deep learning based basketball video analysis for intelligent arena application

Given the tremendous growth of sport fans, the "Intelligent Arena", which can greatly improve the fun of traditional sports, becomes one of the new-emerging applications and research topics. The development of multimedia computing and artificial intelligence technologies support intelligent sport video analysis to add live video broadcast, score detection, highlight video generation, and online sharing functions to the intelligent arena applications. In this paper, we have proposed a deep learning based video analysis scheme for intelligent basketball arena applications. First of all, with multiple cameras or mobile devices capturing the activities in arena, the proposed scheme can automatically select the camera to give high-quality broadcast in real-time. Furthermore, with basketball energy image based deep conventional neural network, we can detect the scoring clips as the highlight video reels to support the wonderful actions replay and online sharing functions. Finally, evaluations on a built real-world basketball match dataset demonstrate that the proposed system can obtain 94.59% accuracy with only less than 45ms processing time (i.e., 10ms broadcast camera selection, and 35ms for scoring detection) for each frame. As the outstanding performance, the proposed deep learning based basketball video analysis scheme is implemented into a commercial intelligent basketball arena application named "Standz Basketball". Although the application had been only released for one month, it achieves the 85th day download ranking place in the sport category of Chinese iTunes market.

Research on Video Game Scene Annotation in Basketball Video

Research on Video Game Scene Annotation in Basketball Video

Behavior prediction of ball carriers in basketball match video

This paper proposes a new method of behavior prediction of ball carriers in the basket-ball match video. Due to the low resolution of the head images, the fast motion of sportsmen and the clustered background in the video, this paper proposes the adoption of a covariance descriptor to fuse multiple visual features of the head region, which can be represented as Riemannian Manifolds. Then map the covariance descriptor to the tangent space and complete the head pose classification through the trained multiclass Logitboosts directly in this space for determination of the vision field. According to the distribution of all the sportsmen with-in the range of vision of the ball carrier, this paper predicts his behaviors of shooting, passing and dribbling through sportsmen information based on artificial potential field(APF). At last, this paper testifies the effectiveness of the proposed method by testing on the basketball match videos.

Court Reconstruction for Camera Calibration in Broadcast Basketball Videos.

We introduce a technique of calibrating camera motions in basketball videos. Our method particularly transforms player positions to standard basketball court coordinates and enables applications such as tactical analysis and semantic basketball video retrieval. To achieve a robust calibration, we reconstruct the panoramic basketball court from a video, followed by warping the panoramic court to a standard one. As opposed to previous approaches, which individually detect the court lines and corners of each video frame, our technique considers all video frames simultaneously to achieve calibration; hence, it is robust to illumination changes and player occlusions. To demonstrate the feasibility of our technique, we present a stroke-based system that allows users to retrieve basketball videos. Our system tracks player trajectories from broadcast basketball videos. It then rectifies the trajectories to a standard basketball court by using our camera calibration method. Consequently, users can apply stroke queries to indicate how the players move in gameplay during retrieval. The main advantage of this interface is an explicit query of basketball videos so that unwanted outcomes can be prevented. We show the results in Figs. 1, 7, 9, 10 and our accompanying video to exhibit the feasibility of our technique.

Research on Tracking Technology in Basketball Video

Research on Tracking Technology in Basketball Video

Research on Long Shot Segmentation in Basketball Video

In the basketball segmentation in long shots, Gauss filter is adopted to smooth noise in the image firstly. Secondly, the background is separated by the difference of inter-frame and the connected regions are labeled. Thirdly, a strategy is designed to identify basketball by the characteristic of basketball. Finally, the edge deviation is revised and the optimal result is obtained using improved Snakes.

Research on Scoreboard Detection and Localization in Basketball Video

Research on Scoreboard Detection and Localization in Basketball Video

Extending the theory of Bounded Generalized Reciprocity: An explanation of the social benefits of cooperative video game play

The theory of Bounded Generalized Reciprocity was examined to help explain why cooperative video game play can increase players’ subsequent pro-social behaviors. Participants played a basketball video game with a helpful or unhelpful teammate against an ostensible opposing team. Participants in the control condition did not play a video game with their teammate. Participants then engaged in a one-shot simultaneous or sequential prisoner’s dilemma game with their teammate and an opposing team member. In line with Bounded Generalized Reciprocity, donations to teammates were influenced by expectations of others to reciprocate pro-social behaviors. Specifically, playing with a helpful teammate confirmed expectations of in-group members to reciprocate pro-social behaviors and led to increases in pro-social behaviors between teammates. Playing with an unhelpful teammate disconfirmed expectations of in-group members to reciprocate pro-social behaviors and led to decreases in pro-social behaviors between teammates. Interestingly, playing with a helpful teammate increased participants’ donation to out-group members even though participants did not expect them to reciprocate. The current study emphasizes the importance of pro-social reciprocity expectations in predicting people’s pro-social behaviors and the impact cooperative video game play can have on such expectations.