Semantic analysis of field sports video using a petri-net of audio-visual concepts

The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework

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

Editorial: performance analysis in sport

Performance analysis is a sub-discipline of Sport Science research (Borms, 2008) that has attained great interest for many stakeholders (i.e., coaches, technical staff, performance analysts, managers, media, fans, and players) at different levels of performance (i.e., youth, semiprofessional, or professional players). The development and implementation of new technologies to measure individual or team’s performances (e.g., tracking systems such as local positioning systems, LPS; video tracking, or observational video analysis systems) with multiple practical applications have intensified the focus of performance analysis in sport (Hughes and Franks, 2007). Traditional approaches have included static analysis focused on retrospective performances; however, dynamic and complex analyses (i.e., non-linear Multi-Dimensional Scaling, classification and regression tree, logistic regression, etc.) have become increasingly utilized by researchers for a deeper understanding of sport performance during training and competition (O’Donoghue, 2009). In particular, a holistic and multidisciplinary perspective such as the Grand Unified Theory analyses (GUT, see Glazier, 2017) has been suggested to be fundamental for sports performance. This approach, provides a framework to examine the inter- and intra-athlete’s behavior dimensions under the environmental and task-related (ecological) factors that affect the performance. Specifically, isolated approaches have been suggested to be avoided with the integration of the biomechanical, physiological, psychological, technical, tactical, positional, motor development and/or strength and conditioning perspectives recommended when evaluating match-related contexts and training tasks (Glazier, 2017). Additionally, Woods et al. (2020) highlighted the importance of ecological dynamics to guide the control, preparation and assessment of athletes and teams. Subsequently, the use of interdisciplinary research designs would provide clear and well-described rationales, powerful data collection and analyses, resulting in robust findings. Innovative sports performance analyses that incorporate new technologies to understand individual’s behaviors within real-based and ecological contexts would provide a greater understanding of how players and teams act and react for greater performance development and application (Bertollo et al., 2020). In fact, as Robertson (2020) argued, the development of professionalism and data gathering in sport had lead to a new scenario for coaching staff, athletes, and performance analysts where adaptative tools are essentially required to understand the needs of sports performance (e.g., human-machine interaction, perspective, innovation, versatility, visualization, evaluation, feedback, generalization, and future planning

Multi-Person Tracking By Multi-Scale Detection in Basketball Scenarios

Tracking data is a powerful tool for basketball teams in order to extract advanced semantic information and statistics that might lead to a performance boost. However, multi-person tracking is a challenging task to solve in single-camera video sequences, given the frequent occlusions and cluttering that occur in a restricted scenario. In this paper, a novel multi-scale detection method is presented, which is later used to extract geometric and content features, resulting in a multi-person video tracking system. Having built a dataset from scratch together with its ground truth (more than 10k bounding boxes), standard metrics are evaluated, obtaining notable results both in terms of detection (F1-score) and tracking (MOTA). The presented system could be used as a source of data gathering in order to extract useful statistics and semantic analyses a posteriori