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This paper presents a real-time computer vision system that tracks the motion of a tennis ball in 3 0 using multiple cameras. Ball tracking enables virtual replays, new game statistics, and other visualizations which result in very new ways of experiencing and analyzing tennis matches. The system has been used in international television broadcasts and webcasts of more than 15 matches. Six cameras around a stadium, divided into four pairs, are currently used to track the ball on serves which sometimes exceed speeds of 225 kmph. A multi-threaded approach is taken to tracking where each thread tracks the ball in a pair of cameras based on motion, intensity and shape, performs stereo matching CO obtain the 30 trajectory, detects when a ball goes out of view of its camera pair, and initializes and triggers a subsequent thread. This efSlcient approach is scalable to many more cameras tracking multiple objects. The ready acceptance of the system indicates the growing potential for multi-camera based real-time tracking in broadcast applications.

We introduce a new paradigm for real-time conversion of a real world event into a rich multimedia database by processing data from multiple sensors observing the event. Real-time analysis of the sensor data, tightly coupled with domain knowledge, results in instant indexing of multimedia data at capture time. This yields semantic information to answer complex queries about the content and the ability to extract portions of data that correspond to complex actions performed in the real world. The power of such an instantly indexed multimedia database system, in content-based retrieval of multimedia data or in semantic analysis and visualization of the data, far exceeds that of systems that index multimedia data only after it is produced.We present LucentVision, an instantly indexed multimedia database system developed for the sport of tennis. This system analyzes video from multiple cameras in real time and captures the activity of the players and the ball in the form of motion trajectories. The system stores these trajectories in a database along with video, three-dimensional (3-D) models of the environment, scores, and other domain-specific information. LucentVision has been used to enhance live television and Internet broadcasts with game analysis and virtual replays in more than 250 international tennis matches. Index Terms-Broadcast, content-based retrieval, Internet, television, tracking, video, vision, visual data mining, visualization.1520-9210/02$17.00 © 2002 IEEE

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ABSTRACT:The image of an object and of the shadow it casts on a planar surface provides important cues for three-dimensional (3D) stance recovery. We assume that the position of the plane on which the shadow lies with respect to a pinhole camera is known and that the position of the light source is unknown. If the light source is sufficiently far away that parallel projection may be assumed, then knowledge of two point correspondences between images of feature points and images of their shadows is enough to determine the position of the object and the direction of the light source. If the light source is close enough that the shadow points are obtained via perspective projection, then there is a one-parameter infinite family of solutions for the position of the object and the light source. Determining the stance of an object is highly sensitive to noise, so we provide algorithms for stance recovery that take into account known information about the object. In our experiments, the errors for the location of the 3D feature points obtained by these algorithms are generally less than 0.2% times the error in pixels in the image points and the errors for the 3D directions of the links is roughly 0.04°times the error in pixels, normalized by the distance to the object from the camera and the length of the link.