Proceedings Article•
Performance analysis and visualisation in tennis using a low-cost camera network
01 Oct 2010-
TL;DR: A novel system for tennis performance analysis that allows coaches to review games and provide detailed audio-visual feedback to tennis athletes and can be generalised to other sports and allow a range of non-professional sports clubs to provide high-quality feedback to their athletes.
Abstract: We describe a novel system for tennis performance analysis that allows coaches to review games and provide detailed audio-visual feedback to tennis athletes. The basis for our system is a network of low-cost IP cameras surrounding the tennis court. Our system exploits the output of several visual analysis modules, including the tracking of players and the tennis ball, and the extraction of player silhouettes for 3D reconstruction. A range of intuitive tools within the interface allow tennis coaches to add 2D and 3D annotations to live video, view play from multiple perspectives, record audio commentary and compute game statistics in real-time. The result is a video file that can be used to provide personalised feedback to the players or for use as a teaching resource for others. While we focus on tennis in this work, we believe our system can be generalised to other sports and allow a range of non-professional sports clubs to provide high-quality feedback to their athletes.
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TL;DR: The results show that the proposed framework can be utilized to model an adequate soft biometric system with rank-1 identification rate of 88%.
27 citations
TL;DR: A system is proposed in which information provided by face recognition and gait analysis is fused to identify people, which has direct industrial applications since it is a non-intrusive security system that is reliable, easily deployable and just needs an inexpensive camera.
Abstract: According to a new market research report, electronic access control system is expected to be worth $16.3 billion by 2017. Vision-based biometric authentication systems have received much attention with increasing demands for long distance surveillance applications and access control to the security area. Such visual application is mainly focused on face recognition. Nevertheless, small size or poor quality images with varying poses, illumination, expressions, glasses or hats and so on can perturb the recognition. To deal with such problems, the fusing of both face recognition and gait analysis is proposed. Gait has recently become one of the most emerging biometrics for non-intrusive person identification. A system is proposed in which information provided by face recognition and gait analysis is fused to identify people. Such a system has direct industrial applications since it is a non-intrusive security system that is reliable, easily deployable and just needs an inexpensive camera.
15 citations
TL;DR: This work reviews a detailed discussion on some of the artificial intelligence (AI) applications in sports vision, GPU-based work stations, and embedded platforms, and identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.
Abstract: Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting the trajectories of player or ball, recognizing the team‟s strategies, classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher‟s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been provided. This work reviews a detailed discussion on some of the artificial intelligence (AI) applications in sports vision, GPU-based work stations, and embedded platforms. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.
14 citations
TL;DR: A new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate is proposed.
Abstract: Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames.
12 citations
Cites background from "Performance analysis and visualisat..."
...Although improving the contrast consequently results in considering incidental pixels, due to shadow cast, as part of the foreground image, in order to remove the shadows that are extracted with the object, a color and brightness difference, B D , is computed as [22]...
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07 Sep 2011
TL;DR: This work develops image processing and computer vision techniques for visually tracking a tennis ball, in 3D, on a court instrumented with multiple low-cost IP cameras, and incorporates a physics-based trajectory model into the system.
Abstract: In this work, we develop image processing and computer vision techniques for visually tracking a tennis ball, in 3D, on a court instrumented with multiple low-cost IP cameras The technique first obtains 2D ball tracking data from each camera view using 2D object tracking methods Next, an automatic feature-based video synchronization method is applied This technique uses the extracted 2D ball information from two or more camera views, plus camera calibration information In order to find 3D trajectory, the temporal 3D locations of the ball is estimated using triangulation of correspondent 2D locations obtained from automatically synchronized videos Furthermore, in order to improve the continuity of the tracked 3D ball during times when no two cameras have overlapping views of the ball location, we incorporate a physics-based trajectory model into the system The resultant 3D ball tracks are then visualized in a virtual 3D graphical environment Finally, we quantify the accuracy of our system in terms of reprojection error
10 citations
Cites background or methods from "Performance analysis and visualisat..."
...Figure 2 represents our system at a block level....
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...This 3D data can be used for analysis purposes such as determining the speed of the ball over the net (a common tennis coach requirement), classification of type of shots played by the players, or to index the video frames and classify important events for coaching [1]....
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