Indoor Activity Detection and Recognition for Sport Games Analysis

Activity recognition in sport is an attractive field for computer vision research. Game, player and team analysis are of great interest and research topics within this field emerge with the goal of automated analysis. The very specific underlying rules of sports can be used as prior knowledge for the recognition task and present a constrained environment for evaluation. This paper describes recognition of single player activities in sport with special emphasis on volleyball. Starting from a per-frame player-centered activity recognition, we incorporate geometry and contextual information via an activity context descriptor that collects information about all player's activities over a certain timespan relative to the investigated player. The benefit of this context information on single player activity recognition is evaluated on our new real-life dataset presenting a total amount of almost 36k annotated frames containing 7 activity classes within 6 videos of professional volleyball games. Our incorporation of the contextual information improves the average player-centered classification performance of 77.56% by up to 18.35% on specific classes, proving that spatio-temporal context is an important clue for activity recognition.Comment: Part of the OAGM 2014 proceedings (arXiv:1404.3538

Impact of National Culture on the Bonus’ Use for Teamwork

Today, organizations use teams as primary work units adopting team rewards and incentives in which group members’ pay is at least partly contingent on measurable group performance. It is the process of compensating a group of employees based on their combined contribution to a particular project or goal. They could be monetary (for example: team bonuses, team commission, shopping vouchers for each team member, etc.) and nonmonetary (team celebration—gateaway bonding activity, team dinner, tickets to a sports event etc., team trip/holiday—may include spouses, team merchandise—team jacket, pin, emblem to build team identity, recognition certificates, team recognition award—public mention and appreciation, team time off away from work). This chapter overviews the empirical research on team-based bonuses and aims to understand if cultural dimensions can interfere or facilitate the diffusion of bonus for teams and suggests directions for future research. The analysis demonstrates that culture may play a critical role in the success of team-based reward programs or in the employee resistance to teams

Targeting Mr Average: Participation, gender equity and school sport partnerships

The School Sport Partnership Programme (SSPP) is one strand of the national strategy for physical education and school sport in England, the physical education and school sport Club Links Strategy (PESSCL). The SSPP aims to make links between school physical education (PE) and out of school sports participation, and has a particular remit to raise the participation levels of several identified under-represented groups, of which girls and young women are one. National evaluations of the SSPP show that it is beginning to have positive impacts on young people's activity levels by increasing the range and provision of extra curricular activities (Office for Standards in Education (OFSTED), 2003, 2004, 2005; Loughborough Partnership, 2005, 2006). This paper contributes to the developing picture of the phased implementation of the programme by providing qualitative insights into the work of one school sport partnership with a particular focus on gender equity. The paper explores the ways in which gender equity issues have been explicitly addressed within the 'official texts' of the SSPP; how these have shifted over time and how teachers are responding to and making sense of these in their daily practice. Using participation observation, interview and questionnaire data, the paper explores how the coordinators are addressing the challenge of increasing the participation of girls and young women. The paper draws on Walby's (2000) conceptualisation of different kinds of feminist praxis to highlight the limitations of the coordinators' work. Two key themes from the data and their implications are addressed: the dominance of competitive sport practices and the PE professionals' views of targeting as a strategy for increasing the participation of under-represented groups. The paper concludes that coordinators work within an equality or difference discourse with little evidence of the transformative praxis needed for the programme to be truly inclusive. © 2008 Taylor & Francis

The role of motion analysis in elite soccer

The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game especially due to the increased physical demands of match-play. The monitoring of players’ work-rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university- based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, identify practical implications of the established body of knowledge, while highlighting areas that require further exploration

Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks

Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning. A significant part of this research deals with (human) activity recognition (HAR) since humans, and the activities that they perform, generate most of the video semantics. Video-based HAR has applications in various domains, but one of the most important and challenging is HAR in sports videos. Some of the major issues include high inter- and intra-class variations, large class imbalance, the presence of both group actions and single player actions, and recognizing simultaneous actions, i.e., the multi-label learning problem. Keeping in mind these challenges and the recent success of CNNs in solving various computer vision problems, in this work, we implement a 3D CNN based multi-label deep HAR system for multi-label class-imbalanced action recognition in hockey videos. We test our system for two different scenarios: an ensemble of $k$ binary networks vs. a single $k$-output network, on a publicly available dataset. We also compare our results with the system that was originally designed for the chosen dataset. Experimental results show that the proposed approach performs better than the existing solution.Comment: Accepted to IEEE/ACIS SNPD 2018, 6 pages, 3 figure

The THUMOS Challenge on Action Recognition for Videos "in the Wild"

Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition. Until then, video action recognition, including THUMOS challenge, had focused primarily on the classification of pre-segmented (i.e., trimmed) videos, which is an artificial task. In THUMOS 2014, we elevated action recognition to a more practical level by introducing temporally untrimmed videos. These also include `background videos' which share similar scenes and backgrounds as action videos, but are devoid of the specific actions. The three editions of the challenge organized in 2013--2015 have made THUMOS a common benchmark for action classification and detection and the annual challenge is widely attended by teams from around the world. In this paper we describe the THUMOS benchmark in detail and give an overview of data collection and annotation procedures. We present the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal detection. We also present results of submissions to the THUMOS 2015 challenge and review the participating approaches. Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos. We conclude by proposing several directions and improvements for future THUMOS challenges.Comment: Preprint submitted to Computer Vision and Image Understandin