A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches

Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.This research was funded by the Spanish Ministry of Science and Innovation and FEDER funds, grant number PID2021-122136OB-C21, MCIN/AEI/10.13039/501100011033/FEDER, UE

A query language for exploratory analysis of video-based tracking data in padel matches

Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of- the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.Postprint (published version

Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods

The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER under grant TIN2017-88515-C2-1-R

Localización de fugas en redes de distribución de agua mediante k-NN con distancia cosenoidal

Se propone la localización de fugas en redes de distribución de agua medianteclasificadores basados en el método de los vecinos más cercanos (k-NN) con métrica de distanciacosenoidal. El uso de distancias cosenoidales mejora la respuesta del clasificador, con relaciónal que usa métrica Euclidiana. Comparado con las técnicas de localización de fugas basadasen la máxima correlación de los residuos, se consigue una mayor robustez en condicionesaltamente ruidosas, y una menor dependencia del modelo hidráulico de la red, lo que facilita suimplementación, pues no requiere del cálculo de la matriz de sensibilidad. La técnica propuestase programó en MATLABR©y se probó con datos sintéticos obtenidos de simulaciones conEPANET. La evaluación del desempeño reportada se basa en el índice de pérdidas (la fracciónde fugas localizadas erróneamente) y en una medida del error de localización obtenida de ladistancia topológicaPeer ReviewedPostprint (published version

Fox, Naomi

Trabajo presentado en el 6th International Conference on Control, Decision and Information Technologies (CoDIT), celebrado en París (Francia), del 23 al 26 de abril de 2019This paper explores the use of deep learning for leak localization in Water Distribution Networks (WDNs) using pressure measurements. By using a training data set including enough samples of all possible leak localizations, a Convolutional Neural Network(CNN) can be used to learn the different pressure maps that characterized each leak localization. The generalization accuracy has validated and evaluated by means of a testing data set. All of considered training, validation,and also testing data include leak size uncertainty, nodal water demand uncertainty and sensor noise. An innovative approach is proposed to convert every pressure residuals map to an image in order to apply a CNN. In addition with the purpose of filtering the effects of uncertainty and noise a time horizon Bayesian reasoning approach is used over each time instant classification output by the CNN. The Hanoi District Metered Area (DMA) is considered as a case study to illustrate the performance of the proposed leak localization method

Leak localization in water distribution networks using deep learning

This paper explores the use of deep learning for leak localization in Water Distribution Networks (WDNs) using pressure measurements. By using a training data set including enough samples of all possible leak localizations, a Convolutional Neural Network(CNN) can be used to learn the different pressure maps that characterized each leak localization. The generalization accuracy has validated and evaluated by means of a testing data set. All of considered training, validation,and also testing data include leak size uncertainty, nodal water demand uncertainty and sensor noise. An innovative approach is proposed to convert every pressure residuals map to an image in order to apply a CNN. In addition with the purpose of filtering the effects of uncertainty and noise a time horizon Bayesian reasoning approach is used over each time instant classification output by the CNN. The Hanoi District Metered Area (DMA) is considered as a case study to illustrate the performance of the proposed leak localization method.Peer Reviewe

Localización de fugas en redes de distribución de agua mediante k-NN con distancia cosenoidal

Trabajo presentado en el Congreso Nacional de Control Automático 2019 (CNCA 2019) celebrado en la ciudad de Puebla, Puebla, México del 23 al 25 de octubre de 2019. El congreso se llevó a cabo en las instalaciones de la Facultad de Ciencias de la Electrónica de la Benemérita Universidad Autónoma de Puebla (FCE-BUAP).Se propone la localización de fugas en redes de distribución de agua mediante clasificadores basados en el método de los vecinos más cercanos (k-NN) con métrica de distancia cosenoidal. El uso de distancias cosenoidales mejora la respuesta del clasificador, con relación al que usa métrica Euclidiana. Comparado con las técnicas de localización de fugas basadas en la máxima correlación de los residuos, se consigue una mayor robustez en condiciones altamente ruidosas, y una menor dependencia del modelo hidráulico de la red, lo que facilita su implementación, pues no requiere del cálculo de la matriz de sensibilidad. La técnica propuesta se programó en MATLAB y se probó con datos sintéticos obtenidos de simulaciones con EPANET. La evaluación del desempeño reportada se basa en el índice de pérdidas (la fracción de fugas localizadas erróneamente) y en una medida del error de localización obtenida de la distancia topológica.Esta investigación fue financiada por el Consejo Nacional de Ciencia y Tecnología (CONACYT) a través de la convocatoria Atención a Problemas Nacionales, con num. de proyecto PN-2016/3595.Peer reviewe

Estimating player positions from padel high-angle videos: Accuracy comparison of recent computer vision methods

The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER under grant TIN2017-88515-C2-1-R.Peer ReviewedPostprint (published version

Localización de fugas en redes de distribución de agua mediante k-NN con distancia cosenoidal

Se propone la localización de fugas en redes de distribución de agua medianteclasificadores basados en el método de los vecinos más cercanos (k-NN) con métrica de distanciacosenoidal. El uso de distancias cosenoidales mejora la respuesta del clasificador, con relaciónal que usa métrica Euclidiana. Comparado con las técnicas de localización de fugas basadasen la máxima correlación de los residuos, se consigue una mayor robustez en condicionesaltamente ruidosas, y una menor dependencia del modelo hidráulico de la red, lo que facilita suimplementación, pues no requiere del cálculo de la matriz de sensibilidad. La técnica propuestase programó en MATLABR©y se probó con datos sintéticos obtenidos de simulaciones conEPANET. La evaluación del desempeño reportada se basa en el índice de pérdidas (la fracciónde fugas localizadas erróneamente) y en una medida del error de localización obtenida de ladistancia topológicaPeer Reviewe

Localización de fugas en redes de distribución de agua mediante k-NN con distancia cosenoidal

Se propone la localización de fugas en redes de distribución de agua medianteclasificadores basados en el método de los vecinos más cercanos (k-NN) con métrica de distanciacosenoidal. El uso de distancias cosenoidales mejora la respuesta del clasificador, con relaciónal que usa métrica Euclidiana. Comparado con las técnicas de localización de fugas basadasen la máxima correlación de los residuos, se consigue una mayor robustez en condicionesaltamente ruidosas, y una menor dependencia del modelo hidráulico de la red, lo que facilita suimplementación, pues no requiere del cálculo de la matriz de sensibilidad. La técnica propuestase programó en MATLABR©y se probó con datos sintéticos obtenidos de simulaciones conEPANET. La evaluación del desempeño reportada se basa en el índice de pérdidas (la fracciónde fugas localizadas erróneamente) y en una medida del error de localización obtenida de ladistancia topológicaPeer Reviewe