Long Sha

Swimmer Localization from a Moving Camera

At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.

Chalkboarding: A New Spatiotemporal Query Paradigm for Sports Play Retrieval

The recent explosion of sports tracking data has dramatically increased the interest in effective data processing and access of sports plays (i.e., short trajectory sequences of players and the ball). And while there exist systems that offer improved categorizations of sports plays (e.g., into relatively coarse clusters), to the best of our knowledge there does not exist any retrieval system that can effectively search for the most relevant plays given a specific input query. One significant design challenge is how best to phrase queries for multi-agent spatiotemporal trajectories such as sports plays.We have developed a novel query paradigm and retrieval system, which we call Chalkboarding, that allows the user to issue queries by drawing a play of interest (similar to how coaches draw up plays). Our system utilizes effective alignment, templating, and hashing techniques tailored to multi-agent trajectories, and achieves accurate play retrieval at interactive speeds.We showcase the efficacy of our approach in a user study, where we demonstrate orders-of-magnitude improvements in search quality compared to baseline systems.

Understanding and analyzing a large collection of archived swimming videos

In elite sports, nearly all performances are captured on video. Despite the massive amounts of video that has been captured in this domain over the last 10-15 years, most of it remains in an “unstructured” or “raw” form, meaning it can only be viewed or manually annotated/tagged with higher-level event labels which is time consuming and subjective. As such, depending on the detail or depth of annotation, the value of the collected repositories of archived data is minimal as it does not lend itself to large-scale analysis and retrieval. One such example is swimming, where each race of a swimmer is captured on a camcorder and in-addition to the split-times (i.e., the time it takes for each lap), stroke rate and stroke-lengths are manually annotated. In this paper, we propose a vision-based system which effectively “digitizes” a large collection of archived swimming races by estimating the location of the swimmer in each frame, as well as detecting the stroke rate. As the videos are captured from moving hand-held cameras which are located at different positions and angles, we show our hierarchical-based approach to tracking the swimmer and their different parts is robust to these issues and allows us to accurately estimate the swimmer location and stroke rates.

Large-Scale Adversarial Sports Play Retrieval with Learning to Rank

As teams of professional leagues are becoming more and more analytically driven, the interest in effective data management and access of sports plays has dramatically increased. In this article, we present a retrieval system that can quickly find the most relevant plays from historical games given an input query. To search through a large number of games at an interactive speed, our system is built upon a distributed framework so that each query-result pair is evaluated in parallel. We also propose a pairwise learning to rank approach to improve search ranking based on users’ clickthrough behavior. The similarity metric in training the rank function is based on automatically learnt features from a convolutional autoencoder. Finally, we showcase the efficacy of our learning to rank approach by demonstrating rank quality in a user study.

Interactive Sports Analytics: An Intelligent Interface for Utilizing Trajectories for Interactive Sports Play Retrieval and Analytics

Analytics in professional sports has experienced a dramatic growth in the last decade due to the wide deployment of player and ball tracking systems in team sports, such as basketball and soccer. With the massive amount of fine-grained data being generated, new data-points are being generated, which can shed light on player and team performance. However, due to the complexity of plays in continuous sports, these data-points often lack the specificity and context to enable meaningful retrieval and analytics. In this article, we present an intelligent human--computer interface that utilizes trajectories instead of words, which enables specific play retrieval in sports. Various techniques of alignment, templating, and hashing were utilized by our system and they are tailored to multi-agent scenario so that interactive speeds can be achieved. We conduct a user study to compare our method to the conventional keywords-based system and the results show that our method significantly improves the retrieval quality. We also show how our interface can be utilized for broadcast purposes, where a user can draw and interact with trajectories on a broadcast view using computer vision techniques. Additionally, we show that our method can also be used for interactive analytics of player performance, which enables the users to move players around and see how performance changes as a function of position and proximity to other players.