MMSports '19最新文献

Detection of Tennis Events from Acoustic Data 基于声学数据的网球赛事检测

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355520

Aaron K. Baughman, Eduardo Morales, G. Reiss, Nancy Greco, Stephen Hammer, Shiqiang Wang

{"title":"Detection of Tennis Events from Acoustic Data","authors":"Aaron K. Baughman, Eduardo Morales, G. Reiss, Nancy Greco, Stephen Hammer, Shiqiang Wang","doi":"10.1145/3347318.3355520","DOIUrl":"https://doi.org/10.1145/3347318.3355520","url":null,"abstract":"Professional tennis is a fast-paced sport with serves and hits that can reach speeds of over 100 mph and matches lasting long in duration. For example, in 13 years of Grand Slam data, there were 454 matches with an average of 3 sets that lasted 40 minutes. The fast pace and long duration of tennis matches make tracking the time boundaries of each tennis point in a match challenging. The visual aspect of a tennis match is highly diverse because of its variety in angles, occlusions, resolutions, contrast and colors, but the sound component is relatively stable and consistent. In this paper, we present a system that detects events such as ball hits and point boundaries in a tennis match from sound data recorded in the match. We first describe the sound processing pipeline that includes preprocessing, feature extraction, basic (atomic) event detection, and point boundary detection. Then, we describe the overall cloud-based system architecture. Afterwards, we describe the user interface that includes a tool for data labeling to efficiently generate the training dataset, and a workbench for sound and model management. The performance of our system is evaluated in experiments with real-world tennis sound data. Our proposed pipeline can detect atomic tennis events with an F1-score of 92.39% and point boundaries with average precision and recall values of around 80%. This system can be very useful for tennis coaches and players to find and extract game highlights with specific characteristics, so that they can analyze these highlights and establish their play strategy.","PeriodicalId":322390,"journal":{"name":"MMSports '19","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127205146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Combining Qualitative and Quantitative Analysis in Football with SportSense 足球定性与定量分析与运动感知的结合

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355519

Philipp Seidenschwarz, Adalsteinn Jonsson, Fabian Rauschenbach, M. Rumo, L. Probst, H. Schuldt

{"title":"Combining Qualitative and Quantitative Analysis in Football with SportSense","authors":"Philipp Seidenschwarz, Adalsteinn Jonsson, Fabian Rauschenbach, M. Rumo, L. Probst, H. Schuldt","doi":"10.1145/3347318.3355519","DOIUrl":"https://doi.org/10.1145/3347318.3355519","url":null,"abstract":"The task of performance analysts and coaches in football (and other team sports) is manifold: they need to assess the performance of individual players of their team, they need to monitor the interaction between players of their team and their tactical compliance, and they need to analyze other teams. For this, they usually have to consider various sources of information: video footage, tracking data, event data, and aggregated statistics. On the basis of this information, analysts have to generate quantitative summaries of events including their spatial and temporal distribution, and the qualitative assessment of individual events by considering the associated video footage. In this paper, we present SportSense, a system for sports video retrieval, that seamlessly combines quantitative and qualitative analysis. For this, SportSense provides dedicated filters that help analysts in selecting the events they are interested in. Moreover, it supports the comparative analysis of stored queries with respect to specific parameters. Essentially, SportSense allows to easily switch between qualitative and quantitative analyses to support coaches and analysts in a best possible way in their task. Based on a user study, we show the effectiveness of the proposed approach.","PeriodicalId":322390,"journal":{"name":"MMSports '19","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133879407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Empirical Analysis of Pacing in Road Cycling 公路自行车运动节奏的实证分析

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355522

D. Saupe, Alexander Artiga Gonzalez, Ramona Burger, C. Abbiss

引用次数: 0

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355516

T. Haruyama, Sho Takahashi, Takahiro Ogawa, M. Haseyama

引用次数: 3

Spectator Excitement Detection in Small-scale Sports Events 小规模体育赛事观众兴奋度检测

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355521

K. Abe, Chikara Nakamura, Yosuke Otsubo, Tetsuya Koike, N. Yokoya

引用次数: 0

Frame-Level Event Detection in Athletics Videos with Pose-Based Convolutional Sequence Networks 基于姿态卷积序列网络的运动视频帧级事件检测

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355525

Moritz Einfalt, Charles Dampeyrou, D. Zecha, R. Lienhart

引用次数: 19

Prediction of Future Shot Direction using Pose and Position of Tennis Player 利用网球运动员的姿势和位置预测未来击球方向

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355523

Tomohiro Shimizu, Ryo Hachiuma, H. Saito, Takashi Yoshikawa, Chonho Lee

引用次数: 11

Flexible Automatic Football Filming and Summarization 柔性自动足球拍摄和总结

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355526

F. Turchini, Lorenzo Seidenari, L. Galteri, Andrea Ferracani, Giuseppe Becchi, A. Bimbo

{"title":"Flexible Automatic Football Filming and Summarization","authors":"F. Turchini, Lorenzo Seidenari, L. Galteri, Andrea Ferracani, Giuseppe Becchi, A. Bimbo","doi":"10.1145/3347318.3355526","DOIUrl":"https://doi.org/10.1145/3347318.3355526","url":null,"abstract":"We propose a method aimed at reducing human intervention in football video shooting and highlights editing, allowing automatic highlight detection together with panning and zooming on salient areas of the playing field. Our recognition subsystem exploits computer vision algorithms to perform automatic detection, pan and zoom and extraction of salient segments of a recorded match. Matches are elaborated offline, extracting and analyzing motion and visual features of the elements in salient zones of the scene, i.e. midfield circle and penalty areas. Automatic summarization is performed by classifying subsequences of a match with machine learning algorithms, which are pretrained on previously acquired and annotated videos of other matches. Among salient actions, special attention is given to goal events, but also other generic highlights are identified. The only assumption for our method to work is to employ a pair of cameras which should frame the football pitch splitting the field in two halves. We demonstrate the functioning of our approach using two ultra high definition cameras, building a system which is also able to collect various metadata of the matches to extrapolate other salient information.","PeriodicalId":322390,"journal":{"name":"MMSports '19","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125027282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A Deep Architecture for Multimodal Summarization of Soccer Games 足球比赛多模态摘要的深度体系结构

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355524

Melissa Sanabria, Sherly, F. Precioso, Thomas Menguy

{"title":"A Deep Architecture for Multimodal Summarization of Soccer Games","authors":"Melissa Sanabria, Sherly, F. Precioso, Thomas Menguy","doi":"10.1145/3347318.3355524","DOIUrl":"https://doi.org/10.1145/3347318.3355524","url":null,"abstract":"The massive growth of sports videos, specially in soccer, has resulted in a need for the automatic generation of summaries, where the objective is not only to show the most important actions of the match but also to elicit as much emotion as the ones bring upon by human editors. State-of-the-art methods on video summarization mostly rely on video processing, however this is not an optimal approach for long videos such as soccer matches. In this paper we propose a multimodal approach to automatically generate summaries of soccer match videos that consider both event and audio features. The event features get a shorter and better representation of the match, and the audio helps detect the excitement generated by the game. Our method consists of three consecutive stages: Proposals, Summarization and Content Refinement. The first one generates summary proposals, using Multiple Instance Learning to deal with the similarity between the events inside the summary and the rest of the match. The Summarization stage uses event and audio features as input of a hierarchical Recurrent Neural Network to decide which proposals should indeed be in the summary. And the last stage, takes advantage of the visual content to create the final summary. The results show that our approach outperforms by a large margin not only the video processing methods but also methods that use event and audio features.","PeriodicalId":322390,"journal":{"name":"MMSports '19","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133402517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Tracking Jockeys in a Cluttered Environment with Group Dynamics 跟踪骑师在一个混乱的环境与群体动力学

MMSports '19 Pub Date : 2019-10-15 DOI: 10.1145/3347318.3355518

Mohammad Hedayati, M. Cree, Jonathan B. Scott

引用次数: 2