Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns to map a single image to a state in a Newtonian scenario. Our experimental evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian scenarios represented using game engines, and 4516 still images with their ground truth dynamics

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications

MonoTrack: Shuttle trajectory reconstruction from monocular badminton video

Trajectory estimation is a fundamental component of racket sport analytics, as the trajectory contains information not only about the winning and losing of each point, but also how it was won or lost. In sports such as badminton, players benefit from knowing the full 3D trajectory, as the height of shuttlecock or ball provides valuable tactical information. Unfortunately, 3D reconstruction is a notoriously hard problem, and standard trajectory estimators can only track 2D pixel coordinates. In this work, we present the first complete end-to-end system for the extraction and segmentation of 3D shuttle trajectories from monocular badminton videos. Our system integrates badminton domain knowledge such as court dimension, shot placement, physical laws of motion, along with vision-based features such as player poses and shuttle tracking. We find that significant engineering efforts and model improvements are needed to make the overall system robust, and as a by-product of our work, improve state-of-the-art results on court recognition, 2D trajectory estimation, and hit recognition.Comment: To appear in CVSports@CVPR 202

Training Algorithms for Multiple Object Tracking

Multiple object tracking is a crucial Computer Vision Task. It aims at locating objects of interest in the image sequences, maintaining their identities, and identifying their trajectories over time. A large portion of current research focuses on tracking pedestrians, and other types of objects, that often exhibit predictable behaviours, that allow us, as humans, to track those objects. Nevertheless, most existing approaches rely solely on simple affinity or appearance cues to maintain the identities of the tracked objects, ignoring their behaviour. This presents a challenge when objects of interest are invisible or indistinguishable for a long period of time. In this thesis, we focus on enhancing the quality of multiple object trackers by learning and exploiting the long ranging models of object behaviour. Such behaviours come in different forms, be it a physical model of the ball motion, model of interaction between the ball and the players in sports or motion patterns of pedestrians or cars, that is specific to a particular scene. In the first part of the thesis, we begin with the task of tracking the ball and the players in team sports. We propose a model that tracks both types of objects simultaneously, while respecting the physical laws of ball motion when in free fall, and interaction constraints that appear when players are in the possession of the ball. We show that both the presence of the behaviour models and the simultaneous solution of both tasks aids the performance of tracking, in basketball, volleyball, and soccer. In the second part of the thesis, we focus on motion models of pedestrian and car behaviour that emerge in the outdoor scenes. Such motion models are inherently global, as they determine where people starting from one location tend to end up much later in time. Imposing such global constraints while keeping the tracking problem tractable presents a challenge, which is why many approaches rely on local affinity measures. We formulate a problem of simultaneously tracking the objects and learning their behaviour patterns. We show that our approach, when applied in conjunction with a number of state-of-the-art trackers, improves their performance, by forcing their output to follow the learned motion patterns of the scene. In the last part of the thesis, we study a new emerging class of models for multiple object tracking, that appeared recently due to availability of large scale datasets - sequence models for multiple object tracking. While such models could potentially learn arbitrarily long ranging behaviours, training them presents several challenges. We propose a training scheme and a loss function that allows to significantly improve the quality of training of such models. We demonstrate that simply using our training scheme and loss allows to learn scoring function for trajectories, which enables us to outperform state-of-the-art methods on several tracking benchmarks

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)

Application of Artificial Intelligence in Basketball Sport

Basketball is among the most popular sports in the world, and its related industries have also produced huge economic benefits. In recent years, the application of artificial intelligence (AI) technology in basketball has attracted a large amount of attention. We conducted a comprehensive review of the application research of AI in basketball through literature retrieval. Current research focuses on the AI analysis of basketball team and player performance, prediction of competition results, analysis and prediction of shooting, AI coaching system, intelligent training machine and arena, and sports injury prevention. Most studies have shown that AI technology can improve the training level of basketball players, help coaches formulate suitable game strategies, prevent sports injuries, and improve the enjoyment of games. At the same time, it is also found that the number and level of published papers are relatively limited. We believe that the application of AI in basketball is still in its infancy. We call on relevant industries to increase their research investment in this area, and promote the improvement of the level of basketball, making the game increasingly exciting as its worldwide popularity continues to increase

Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning

Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality