A video-based framework for automatic 3d localization of multiple basketball players : a combinatorial optimization approach

Sports complexity must be investigated at competitions; therefore, non-invasive methods are essential. In this context, computer vision, image processing, and machine learning techniques can be useful in designing a non-invasive system for data acquisition that identifies players’ positions in official basketball matches. Here, we propose and evaluate a novel video-based framework to perform automatic 3D localization of multiple basketball players. The introduced framework comprises two parts. The first stage is player detection, which aims to identify players’ heads at the camera image level. This stage is based on background segmentation and on classification performed by an artificial neural network. The second stage is related to 3D reconstruction of the player positions from the images provided by the different cameras used in the acquisition. This task is tackled by formulating a constrained combinatorial optimization problem that minimizes the re-projection error while maximizing the number of detections in the formulated 3D localization problem8286CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão temNão temNão temWe would like to thank the CAPES, FAEPEX, FAPESP, and CNPq for funding their research. This paper has content from master degree’s dissertation previously published (Monezi, 2016) and available onlin

Underwater mobile target tracking with particle filter using an autonomous vehicle

This paper describes an underwater mobile target localization and tracking by using an autonomous surface vehicle for which the successive ranges between the target and the reference are the only information. In a dynamic system, such as range-only single-beacon underwater target tracking, a statespace model can be characterized, where the state vector may include position, and velocity of the mobile underwater target. Moreover, the range observations can come from a mobile autonomous vehicle, which is used as a moving landmark. Then, a nonlinear Bayesian filtering algorithm can be used to make extrapolations on the state vector from the observations, in order to obtain the target position at each instant of time. In this paper we consider the use of Particle Filter (PF) to perform such localization and tracking where its performance and characterization is studied under different scenariosPostprint (published version

Exploring techniques for vision based human activity recognition: Methods, systems, and evaluation

With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activity, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation towards the performance of human activity recognitio

Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach

Take your Eyes off the Ball: Improving Ball-Tracking by Focusing on Team Play

Accurate video-based ball tracking in team sports is important for automated game analysis, and has proven very difficult because the ball is often occluded by the players. In this paper, we propose a novel approach to addressing this issue by formulating the tracking in terms of deciding which player, if any, is in possession of the ball at any given time. This is very different from standard approaches that first attempt to track the ball and only then to assign possession. We will show that our method substantially increases performance when applied to long basketball and soccer sequences

Hockey Pose Estimation and Action Recognition using Convolutional Neural Networks to Ice Hockey

Human pose estimation and action recognition in ice hockey are one of the biggest challenges in computer vision-driven sports analytics, with a variety of difficulties such as bulky hockey wear, color similarity between ice rink and player jersey and the presence of additional sports equipment used by the players such as hockey sticks. As such, deep neural network architectures typically used for sports including baseball, soccer, and track and field perform poorly when applied to hockey. This research involves the design and implementation of deep neural networks for both pose estimation and action recognition can effectively evaluate the pose and the actions of a hockey player. First, a pre-trained convolutional neural network, known as the stacked hourglass network, is used to determine a hockey player's body placement in video frames, also known as pose estimation. The proposed method provides a tool to analyze the pose of a hockey player via broadcast video which aids in the eventual assessment of a hockey player's speed, shot accuracy, or other metrics. The algorithm demonstrated to be successful since it identifies on average 81.56% of the joints of a hockey player on a set of test images. Furthermore, inspired by the idea that modeling the pose of a hockey stick can improve hockey player pose estimation, a novel deep learning computer vision architecture known as the HyperStackNet has been designed and implemented for joint player and stick pose estimation. In addition to improving player pose estimation, the HyperStackNet architecture enables improved transfer learning from pre-trained stacked hourglass networks trained on a different domain. Experimental results demonstrate that when the HyperStackNet is trained to detect 18 different joint positions on a hockey player (including the hockey stick), the accuracy is 98.8% on the test dataset, thus demonstrating its efficacy for handling complex joint player and stick pose estimation from video. Extending from pose recognition, this research involves the development of an algorithm for accurate recognition of actions for hockey. To perform this action recognition, a convolutional neural network estimates actions through unifying latent pose and action recognition. The action recognition hourglass network, or ARHN, is designed to interpret player actions in ice hockey video using estimated pose. ARHN has three components. The first component is the latent pose estimator, the second transforms latent features to a common frame of reference, and the third performs action recognition. Since no benchmark dataset for pose estimation or action recognition is available for hockey players, we first had to generate such an annotated dataset. Experimental results show action recognition accuracy of 65% for four types of actions in hockey. When similar poses are merged to three and two classes, the accuracy rate increases to 71% and 78%, proving the potential of the methodology for automated action recognition in hockey