Tracking people in a video sequence is a challenging task that has been approached from many perspectives. This task becomes even more complicated when the person to track is a player in a broadcasted sport event, the reasons being the existence of difficulties such as frequent camera movements or switches, total and partial occlusions between players, and blurry frames due to the codification algorithm of the video. This paper introduces a player tracking solution which is both fast and accurate. This allows to track a player precisely in real-time. The approach combines several models that are executed concurrently in a relatively modest hardware, and whose accuracy has been validated against hand-labeled broadcast video sequences. Regarding the accuracy, the tests show that the area under curve (AUC) of our approach is around 0.6, which is similar to generic state of the art solutions. As for performance, our proposal can process high definition videos (1920x1080 px) at 80 fps.
Tracking and identifying players is an important problem in computer vision based ice hockey analytics. Player tracking is a challenging problem since the motion of players in hockey is fast-paced and non-linear. There is also significant player-player and player-board occlusion, camera panning and zooming in hockey broadcast video. Prior published research perform player tracking with the help of handcrafted features for player detection and re-identification. Although commercial solutions for hockey player tracking exist, to the best of our knowledge, no network architectures used, training …
We present a deep recurrent convolutional neural network (CNN) approach to solve the problem of hockey player identification in NHL broadcast videos. Player identification is a difficult computer vision problem mainly because of the players' similar appearance, occlusion, and blurry facial and physical features. However, we can observe players' jersey numbers over time by processing variable length image sequences of players (aka 'tracklets'). We propose an end-to-end trainable ResNet+LSTM network, with a residual network (ResNet) base and a long short-term memory (LSTM) layer, to discover spa…
L3S Research Center（L3S研究中心）；Leibniz University Hannover（汉诺威莱布尼茨大学）；TIB – Leibniz Information Centre for Science and Technology（德国科学技术莱布尼茨信息中心）；German Sport University Cologne（德国科隆体育大学）
Computer-aided support and analysis are becoming increasingly important in the modern world of sports. The scouting of potential prospective players, performance as well as match analysis, and the monitoring of training programs rely more and more on data-driven technologies to ensure success. Therefore, many approaches require large amounts of data, which are, however, not easy to obtain in general. In this paper, we propose a pipeline for the fully-automated extraction of positional data from broadcast video recordings of soccer matches. In contrast to previous work, the system integrates al…
Field Converter: Geometry-Initialized Temporal Residual Refinement for World-Grounded Player Pose Estimation from Soccer Broadcasts
Institut de Biomécanique Humaine Georges Charpak (IBHGC), Arts et Métiers ParisTech（乔治·沙帕克人体生物力学研究所（IBHGC），巴黎高科国立高等工艺学校）；French Football Federation (FFF)（法国足球联合会（FFF））
Puck localization is an important problem in ice hockey video analytics useful for analyzing the game, determining play location, and assessing puck possession. The problem is challenging due to the small size of the puck, excessive motion blur due to high puck velocity and occlusions due to players and boards. In this paper, we introduce and implement a network for puck localization in broadcast hockey video. The network leverages expert NHL play-by-play annotations and uses temporal context to locate the puck. Player locations are incorporated into the network through an attention mechanism …
Continuous football player tracking from discrete broadcast data
University of Oxford（牛津大学）；Pandemic Sciences Institute, University of Oxford（牛津大学大流行科学研究所）；University of Copenhagen（哥本哈根大学）；Imperial College London（帝国理工学院）
Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method…
Concurrent validity of computer-vision artificial intelligence player tracking software using broadcast footage
Australian Catholic University（澳大利亚天主教大学）；Leeds Beckett University（利兹贝克特大学）；Fédération Internationale de Football Association (FIFA)（国际足球联合会（FIFA））；Sheffield Hallam University（谢菲尔德哈勒姆大学）；Victoria University（维多利亚大学）
University of Waterloo（滑铁卢大学）；Stathletes Inc.（Stathletes公司）
Tracking and identifying players is a fundamental step in computer vision-based ice hockey analytics. The data generated by tracking is used in many other downstream tasks, such as game event detection and game strategy analysis. Player tracking and identification is a challenging problem since the motion of players in hockey is fast-paced and non-linear when compared to pedestrians. There is also significant camera panning and zooming in hockey broadcast video. Identifying players in ice hockey is challenging since the players of the same team look almost identical, with the jersey number the…
University of Liège（列日大学）；EVS Broadcast Equipment（EVS广播设备公司）；KAUST（阿卜杜拉国王科技大学）
Soccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera calibration and on its current limitations for the scientific community. More precisely, we tackle the absence of a large-scale calibration dataset and of a public calibration network trained on such a dataset. Specifically, we distill a powerful commercial calibration tool in a …
Vision based player detection is important in sports applications. Accuracy, efficiency, and low memory consumption are desirable for real-time tasks such as intelligent broadcasting and automatic event classification. In this paper, we present a cascaded convolutional neural network (CNN) that satisfies all three of these requirements. Our method first trains a binary (player/non-player) classification network from labeled image patches. Then, our method efficiently applies the network to a whole image in testing. We conducted experiments on basketball and soccer games. Experimental results d…
Tracking players in sports videos is commonly done in a tracking-by-detection framework, first detecting players in each frame, and then performing association over time. While for some sports tracking players is sufficient for game analysis, sports like hockey, tennis and polo may require additional detections, that include the object the player is holding (e.g. racket, stick). The baseline solution for this problem involves detecting these objects as separate classes, and matching them to player detections based on the intersection over union (IoU). This approach, however, leads to poor matc…
University of British Columbia（不列颠哥伦比亚大学）；National University of Defense Technology（国防科学技术大学）
This work addresses camera selection, the task of predicting which camera should be "on air" from multiple candidate cameras for soccer broadcast. The task is challenging because of the scarcity of learning data with all candidate views. Meanwhile, broadcast videos are freely available on the Internet (e.g. Youtube). However, these videos only record the selected camera views, omitting the other candidate views. To overcome this problem, we first introduce a random survival forest (RSF) method to impute the incomplete data effectively. Then, we propose a spatial-appearance heatmap to describe …
Comprehensive understanding of key players and actions in multiplayer sports broadcast videos is a challenging problem. Unlike in news or finance videos, sports videos have limited text. While both action recognition for multiplayer sports and detection of players has seen robust research, understanding contextual text in video frames still remains one of the most impactful avenues of sports video understanding. In this work we study extremely accurate semantic text detection and recognition in sports clocks, and challenges therein. We observe unique properties of sports clocks, which makes it…
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
In this paper, we propose a novel method to register football broadcast video frames on the static top view model of the playing surface. The proposed method is fully automatic in contrast to the current state of the art which requires manual initialization of point correspondences between the image and the static model. Automatic registration using existing approaches has been difficult due to the lack of sufficient point correspondences. We investigate an alternate approach exploiting the edge information from the line markings on the field. We formulate the registration problem as a nearest…
This paper introduces a novel self-learning framework that automates the label acquisition process for improving models for detecting players in broadcast footage of sports games. Unlike most previous self-learning approaches for improving appearance-based object detectors from videos, we allow an unknown, unconstrained number of target objects in a more generalized video sequence with non-static camera views. Our self-learning approach uses a latent SVM learning algorithm and deformable part models to represent the shape and colour information of players, constraining their motions, and learn…
Automated player identification and indexing using two-stage deep learning network
Stevens Institute of Technology（斯蒂文斯理工学院）；University of Alabama（阿拉巴马大学）；Columbia University in the City of New York（纽约市哥伦比亚大学）；The University of Alabama（阿拉巴马大学）
