Soccer video and player position dataset

Pettersen, Svein Arne; Johansen, Dag; Berg-Johansen, Vegard; Gaddam, Vamsidhar Reddy; Mortensen, Asgeir; Langseth, Ragnar; Griwodz, Carsten; Stensland, Håkon Kvale; Halvorsen, Pål

doi:10.1145/2557642.2563677

Cited by 71 publications

(31 citation statements)

References 6 publications

(8 reference statements)

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“…The players of the home team are equipped with body-sensors during the whole game. The body-sensor data and video camera data of the players of the home team are provided in [31]. The x-axis points southwards parallel with the long side of the field, while the y-axis points eastwards parallel with the short edge of the field, as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Census Signal Temporal Logic Inference for Multiagent Group Behavior Analysis

Julius

2018

IEEE Trans. Automat. Sci. Eng.

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In this paper, we define a novel census signal temporal logic (CensusSTL) that focuses on the number of agents in different subsets of a group that complete a certain task specified by the signal temporal logic (STL). CensusSTL consists of an "inner logic" STL formula and an "outer logic" STL formula. We present a new inference algorithm to infer CensusSTL formulae from the trajectory data of a group of agents. We first identify the "inner logic" STL formula and then infer the subgroups based on whether the agents' behaviors satisfy the "inner logic" formula at each time point. We use two different approaches to infer the subgroups based on similarity and complementarity, respectively.The "outer logic" CensusSTL formula is inferred from the census trajectories of different subgroups.We apply the algorithm in analyzing data from a soccer match by inferring the CensusSTL formula for different subgroups of a soccer team.

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Section: Methodsmentioning

confidence: 99%

Census Signal Temporal Logic Inference for Multiagent Group Behavior Analysis

Julius

2018

IEEE Trans. Automat. Sci. Eng.

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“…The initial pilot test generates less than 0.2 Mb/s network traffic. A soccer players dataset, which includes field position, heading, and speed, was collected with a highly accurate tracking system taking samples at 20 Hz [46]. That means a player who participates in a full game generates approximately 108,000 records.…”

Section: Why: the Challenges Of Personal Health Informationmentioning

confidence: 99%

Collecting, exploring and sharing personal data: Why, how and where

Estrada-Galiñanes

Wac

2020

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New, multi-channel personal data sources (like heart rate, sleep patterns, travel patterns, or social activities) are enabled by ever increased availability of miniaturised technologies embedded within smartphones and wearables. These data sources enable personal self-management of lifestyle choices (e.g., exercise, move to a bike-friendly area) and, on a large scale, scientific discoveries to improve health and quality of life. However, there are no simple and reliable ways for individuals to securely collect, explore and share these sources. Additionally, much data is also wasted, especially when the technology provider ceases to exist, leaving the users without any opportunity to retrieve own datasets from "dead" devices or systems. Our research reveals evidence of what we term human data bleeding and offers guidance on how to address current issues by reasoning upon five core aspects, namely technological, financial, legal, institutional and cultural factors. To this end, we present preliminary specifications of an open platform for personal data storage and quality of life research. The Open Health Archive (OHA) is a platform that would support individual, community and societal needs by facilitating collecting, exploring and sharing personal health and QoL data.

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“…This system is based on using the 5.0 GHz Industrial, Scientific, and Medical (ISM) radio band for communication and signal transmissions. With ZXY, each player wears a belt with a transponder placed at his lumbar (Pettersen et al, 2014), and there are six stationary sensors placed at the stadium perimeter. The stationary sensors compute the position data for each belt by advanced vector based processing of the received radio signals.…”

Section: Tracking Using Lpm (Radio Signals) and Gps In A Professiomentioning

confidence: 99%

Quantified Soccer Using Positional Data: A Case Study

et al. 2018

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Performance development in international soccer is undergoing a silent revolution fueled by the rapidly increasing availability of athlete quantification data and advanced analytics. Objective performance data from teams and individual players are increasingly being collected automatically during practices and more recently also in matches after FIFA's 2015 approval of wearables in electronic performance and tracking systems. Some clubs have even started collecting data from players outside of the sport arenas. Further algorithmic analysis of these data might provide vital insights for individual training personalization and injury prevention, and also provide a foundation for evidence-based decisions for team performance improvements. This paper presents our experiences from using a detailed radio-based wearable positioning data system in an elite soccer club. We demonstrate how such a system can detect and find anomalies, trends, and insights vital for individual athletic and soccer team performance development. As an example, during a normal microcycle (6 days) full backs only covered 26% of the sprint distance they covered in the next match. This indicates that practitioners must carefully consider to proximity size and physical work pattern in microcycles to better resemble match performance. We also compare and discuss the accuracy between radio waves and GPS in sampling tracking data. Finally, we present how we are extending the radio-based positional system with a novel soccer analytics annotation system, and a real-time video processing system using a video camera array. This provides a novel toolkit for modern forward-looking soccer coaches that we hope to integrate in future studies.

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Soccer video and player position dataset

Cited by 71 publications

References 6 publications

Census Signal Temporal Logic Inference for Multiagent Group Behavior Analysis

Census Signal Temporal Logic Inference for Multiagent Group Behavior Analysis

Collecting, exploring and sharing personal data: Why, how and where

Quantified Soccer Using Positional Data: A Case Study

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