1,284 research outputs found
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
Mean shift tracking of soccer-players using motion and color likelihood images
To analyze a soccer game, it is useful to gather statistics of the players. In order to do this automatically, players can be tracked on the field. This paper presents an approach for tracking multiple players from a video of a soccer match. Players are tracked using a mean shift algorithm which operates on player likelihood images. These images are created using a combination of motion likelihood, implementing background subtraction, and color likelihood, obtained through classification with a neural network. With this mean shift algorithm, the system is able to track all players ~100% accurate in typical situations with a controlled environment. Two player occlusions are automatically resolved and the algorithm is able to detect whether a player’s track is lost after which a human operator is asked to assign the correct position
A Visualization Framework for Team Sports Captured using Multiple Static Cameras
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errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.DOI: http://dx.doi.org/
10.1016/j.cviu.2013.09.006We present a novel approach for robust localization of multiple people observed using a set of static cameras. We use this
location information to generate a visualization of the virtual offside line in soccer games. To compute the position of the offside line,
we need to localize players' positions, and identify their team roles. We solve the problem of fusing corresponding players' positional
information by finding minimum weight K-length cycles in a complete K-partite graph. Each partite of the graph corresponds to one of
the K cameras, whereas each node of a partite encodes the position and appearance of a player observed from a particular camera.
To find the minimum weight cycles in this graph, we use a dynamic programming based approach that varies over a continuum from
maximally to minimally greedy in terms of the number of graph-paths explored at each iteration. We present proofs for the efficiency
and performance bounds of our algorithms. Finally, we demonstrate the robustness of our framework by testing it on 82,000 frames of
soccer footage captured over eight different illumination conditions, play types, and team attire. Our framework runs in near-real time,
and processes video from 3 full HD cameras in about 0.4 seconds for each set of corresponding 3 frames
Ball 3D Localization From A Single Calibrated Image
Ball 3D localization in team sports has various applications including
automatic offside detection in soccer, or shot release localization in
basketball. Today, this task is either resolved by using expensive multi-views
setups, or by restricting the analysis to ballistic trajectories. In this work,
we propose to address the task on a single image from a calibrated monocular
camera by estimating ball diameter in pixels and use the knowledge of real ball
diameter in meters. This approach is suitable for any game situation where the
ball is (even partly) visible. To achieve this, we use a small neural network
trained on image patches around candidates generated by a conventional ball
detector. Besides predicting ball diameter, our network outputs the confidence
of having a ball in the image patch. Validations on 3 basketball datasets
reveals that our model gives remarkable predictions on ball 3D localization. In
addition, through its confidence output, our model improves the detection rate
by filtering the candidates produced by the detector. The contributions of this
work are (i) the first model to address 3D ball localization on a single image,
(ii) an effective method for ball 3D annotation from single calibrated images,
(iii) a high quality 3D ball evaluation dataset annotated from a single
viewpoint. In addition, the code to reproduce this research is be made freely
available at https://github.com/gabriel-vanzandycke/deepsport.Comment: 9 pages, CVSports202
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