Increase in the number of sport lovers in games like football, cricket, etc. has created a need for digging, analyzing and presenting more and more multidimensional information to them. Different classes of people require different kinds of information and this expands the space and scale of the required information. Tracking of ball movement is of utmost importance for extracting any information from the ball based sports video sequences. Based on the literature survey, we have initially proposed a block diagram depicting different steps and flow of a general tracking process. The paper further follows the same flow throughout. Detection is the first step of tracking. Dynamic and unpredictable nature of ball appearance, movement and continuously changing background make the detection and tracking processes challenging. Due to these challenges, many researchers have been attracted to this problem and have produced good results under specific conditions. However, generalization of the published work and algorithms to different sports is a distant dream. This paper is an effort to present an exhaustive survey of all the published research works on ball tracking in a categorical manner. The work also reviews the used techniques, their performance, advantages, limitations and their suitability for a particular sport. Finally, we present discussions on the published work so far and our views and opinions followed by a modified block diagram of the tracking process. The paper concludes with the final observations and suggestions on scope of future work.

Ball tracking in sports: a survey

This paper presents a novel framework for predicting shot location and type in tennis. Inspired by recent neuroscience discoveries, we incorporate neural memory modules to model the episodic and semantic memory components of a tennis player. We propose a Semi-Supervised Generative Adversarial Network architecture that couples these memory models with the automatic feature learning power of deep neural networks, and demonstrate methodologies for learning player level behavioral patterns with the proposed framework. We evaluate the effectiveness of the proposed model on tennis tracking data from the 2012 Australian Tennis Open and exhibit applications of the proposed method in discovering how players adapt their style depending on the match context.

Memory Augmented Deep Generative Models for Forecasting the Next Shot Location in Tennis

Estimating the metric height of a person from monocular imagery without additional assumptions is ill-posed. Existing solutions either require manual calibration of ground plane and camera geometry, special cameras, or reference objects of known size. We focus on motion cues and exploit gravity on earth as an omnipresent reference 'object' to translate acceleration, and subsequently height, measured in image-pixels to values in meters. We require videos of motion as input, where gravity is the only external force. This limitation is different to those of existing solutions that recover a person's height and, therefore, our method opens up new application fields. We show theoretically and empirically that a simple motion trajectory analysis suffices to translate from pixel measurements to the person's metric height, reaching a MAE of up to 3.9 cm on jumping motions, and that this works without camera and ground plane calibration.

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Gravity as a Reference for Estimating a Person’s Height From Video

We developed a method for the precise estimation of the 3D trajectory of a baseball by modeling the movement of the baseball and estimating the capture delay, using multiple unsynchronized cameras. To develop the proposed algorithm, we mimicked the real-world process of capturing a baseball in simulation space, and analyzed the capture process using a multiple unsynchronized camera system. We represented the movement of the baseball using a piece-wise spline model, and predicted the position of the baseball in the subframes in a manner which is robust to position error and change in direction of movement of the baseball. This method accurately predicts the baseball position over time by modeling the movement of the baseball in a real baseball game environment, and improves the accuracy of the reconstructed 3D baseball trajectories. We defined an objective function to estimate the capture delay, and estimate the optimal capture delay parameter using non-linear optimization method. In addition, we evaluated the performance of the proposed method in simulation space and in a real-world situation. The experimental results show that the proposed method can estimate a 3D baseball trajectory precisely using a multiple unsynchronized camera system and is robust to variations in capture delay, both in the simulation space and in real-world situations.

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Precise 3D Baseball Pitching Trajectory Estimation Using Multiple Unsynchronized Cameras

Tracking a moving object and reconstructing its trajectory can be done with a stereo camera system, since the two cameras enable depth vision. However, such a system would not work if one of the cameras fails to detect the object. If that happens, it would be beneficial if the system could still use the functioning camera to make an approximate trajectory reconstruction.In this study, I have investigated how past observations from a stereo system can be used to recreate trajectories when video from only one of the cameras is available. Several approaches have been implemented and tested, with varying results. The best method was found to be a nearest neighbors-search optimized by a Kalman filter. On a test set with 10000 golf shots, the algorithm was able to create estimations which on average differed around 3.5 meters from the correct trajectory, with better results for trajec-tories originating close to the camera.

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Estimating 3D-trajectories from Monocular Video Sequences

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

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P. Shashidhar Chavan

Papers

3D Estimation and Visualization of Motion in a Multicamera Network for Sports