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A Survey on Content-Aware Video Analysis for Sports IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 99, NO. 9, JANUARY 2017 1 A Survey on Content-aware Video Analysis for Sports Huang-Chia Shih, Member, IEEE Abstract—Sports data analysis is becoming increasingly 1) Panasonic incorporated SAP SE to develop a video-based large-scale, diversified, and shared, but difficulty persists in sports analytics and tracking system. Match Insights is an rapidly accessing the most crucial information. Previous surveys analytics prototype SAP developed with the German have focused on the methodologies of sports video analysis from Football Association for the soccer World Cup 2014 in the spatiotemporal viewpoint instead of a content-based viewpoint, Brazil [7]. and few of these studies have considered semantics. This study 2) Vizrt, short for VisualiZation (in) real time, is a Norwegian develops a deeper interpretation of content-aware sports video analysis by examining the insight offered by research into the company that provides content production, management, structure of content under different scenarios. On the basis of this and distribution tools for the digital media industry. Its insight, we provide an overview of the themes particularly products include applications for creating real-time 3D relevant to the research on content-aware systems for broadcast graphics and maps, visualizing sports analyses, managing sports. Specifically, we focus on the video content analysis media assets, and obtaining single workflow solutions for techniques applied in sportscasts over the past decade from the the digital broadcast industry. Vizrt has a customer base in perspectives of fundamentals and general review, a content more than 100 countries and approximately 600 employees hierarchical model, and trends and challenges. Content-aware distributed at 40 offices worldwide [8], [9]. analysis methods are discussed with respect to object-, event-, and 3) PITCHf/x data set is a public resource presented by context-oriented groups. In each group, the gap between sensation and content excitement must be bridged using proper strategies. MLBAM [10] and Sportvision [11]. Brooks Baseball [12] In this regard, a content-aware approach is required to determine makes systematic changes to this data set to improve its user demands. Finally, the paper summarizes the future trends quality and usability. They manually review the Pitch Info and challenges for sports video analysis. We believe that our by using several parameters of each pitch’s trajectory and findings can advance the field of research on content-aware video validate the parameters against several other sources such analysis for broadcast sports. as video evidence (e.g., pitcher grip and catcher signs) and direct communication with on-field personnel (e.g., Index Terms— Action recognition, content-aware system, pitching coaches, catchers, and the pitchers themselves). content-based multimedia analysis, event detection, semantic The default values of the trajectory data are slightly altered analysis, sports, survey. to align them more closely with the real values. 4) Sportradar [13], a Swiss company, focuses on collecting I. INTRODUCTION and analyzing data related to sports results by collaborating ESEARCH interest in sports content analysis has increased with bookmakers, national soccer associations, and R substantially in recent decades, because of the rapid growth international soccer associations. Their operating activities of video transmission over the Internet and the demand for include the collection, processing, monitoring, and digital broadcasting applications. The massive commercial commercialization of sports data, which result in a diverse appeal of sports programs has become a dominant focus in the portfolio of sports-related live data and digital content. field of entertainment. Research on big data analytics has A. Surveys and Taxonomies attracted much attention to machine learning and artificial intelligence techniques. Accordingly, content analysis of sports Since 2000, sports video analysis has continually drawn media data has garnered attention from various studies in the research attention, leading to a rapid increase in published work last decade. Sports data analysis is becoming large scale, and surveys in this field. In [14], a preliminary survey of sports diversified, and shared. The most pressing problem currently is video analysis was conducted to examine diverse research how to access the most important information in a short time. topics such as tactic summarization, highlight extraction, Because of the massive demand for sports video computer-assisted referral, and content insertion. Kokaram et al. broadcasting, many enterprises such as Bloomberg, SAP, and [15] demonstrated the development trends in the topics of Vizart employ sports content analytics. Content analysis with sports-related indexing and retrieval. They identified two broad big data has used become a major emerging industry. In offline classes of sports, court and table sports and field sports. service, historical records can be used to analyze video content However, this was not a favorable classification for content through machine learning. In online service, discovered latent analysis. An alternative classification criterion, the time- or knowledge can be for real-time tactic recommendation. In point-driven structure in the progress of a game, was then recent years, many books have contributed to the content considered. Sports such as baseball and soccer are time driven analysis of statistics for baseball [1]–[3] and basketball [4]–[6]. because the high-excitement events occur sparsely and At present, several sports intelligence systems and content randomly. Conversely, point-driven sports consist of regular analytics have been developed and applied: events and constructs within a domain-specific scenario. In this IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 99, NO. 9, JANUARY 2017 2 TABLE I Less Condense KEY ISSUES OF PREVIOUS SURVEYS quantities Transcripts Major # of Conclusion Works Years Sport Genres Minor subjects Goal +Semantics (outcomes) subjects ref. What Tactics analysis, highlight In y fo it r c Event m extraction, tracking, a a +Scene labels (where) p t [14] 2003 Comprehensively Event Sports Video Analysis 28 a io computer-assisted C n +Action / Interaction refereeing, content insertion Who Snooker, tennis, Object Feature extraction, event Sports content retrieval badminton, cricket, Object, +Identity (name) detection, video Court/table sports vs. field Large [15] 2006 soccer, American Event, 54 quantities Raw data summarization, indexing, sports football, baseball, Context Sparse and retrieval Bridging the semantic gap Video Clip sumo Football, handball, Fig. 1. Content Pyramid: a hierarchical content model. Intrusive vs. nonintrusive 2010 basketball, squash, [16] Objects Team tracking systems 43 skating, indoor In summary, previous surveys have focused on the Indoor vs. outdoor sports soccer methodologies of sports video analysis from the spatiotemporal Video summarization, Low-level visual features viewpoint instead of a content-based viewpoint. Few of these provision of augmented [17] 2010 Soccer Event vs. high-level semantic 89 information, high-level studies considered semantics. The current study adopted a analysis analysis different framework, developing a proper and deeper Object, Highlights, summarization, General models for soccer [18] 2011 Soccer 28 Event event detection analysis. interpretation of content-aware sports video analysis. Feature extraction, highlight Audio and video features, Specifically, we present the insight offered by research into the [19] 2014 Soccer Event event detection, video 56 textual method integration structure of content under different scenarios. On the basis of summarization this insight, we provide an overview of the themes particularly type of sports, such as tennis and snooker, highlight events relevant to the research on content-aware systems for sports. yield particular points in the game structure. Different from the B. Scope and Organization of the Paper viewpoint of Kokaram et al. [15], Santiago et al. [16] focused on the application of team tracking techniques to sports games. A video analysis system can be viewed as a content They categorized the reviewed systems into —two classes, reasoning system. In this paper, we review the developments in intrusive and nonintrusive. In intrusive systems, tags or sensors sports video analysis, focusing on content-aware techniques are placed on the targets, whereas nonintrusive systems that involve understanding and arranging the video content on introduce no extra objects to the game environment, instead the basis of intrinsic and semantic concepts. We focus on the using vision as the main sensory source and employing image video content analysis applied in sportscasts over the past processing techniques to perform player and team tracking. decade from three aspects—fundamentals and general review, a Intrusive systems are typically not considered highly mature in content hierarchical model, and challenges and future providing high-level information. A domain-specific survey of directions. soccer video analysis was presented in [17]. The authors First, we introduce the fundamentals of content analysis, reviewed studies on soccer video analysis at three semantic namely the concept of the content pyramid, sports genre levels of interpretation: video summarization, provision of classification, and the overall
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