70 research outputs found
The M-components of level sets of continuous functions in WBV
We prove that the topographic map structure of upper semicontinuous functions, defined in terms of classical connected components of its level sets, and of functions of bounded variation (or a generalization, the WBV functions), defined in terms of M-connected components of its level sets, coincides when the function is a continuous function in WBV. Both function spaces are frequently used as models for images. Thus, if the domain [omega] of the image is Jordan domain, a rectangle, for instance, and the image u [member of] C([omega]) [intersection] WBV([omega]) (being constant near [delta omega]), we prove that for almost all levels [lambda] of u, the classical connected components of positive measure of[u [greater than or equal] [lambda]] coincide with the M-components of [u [greater than or equal] [lambda]]. Thus the notion of M-component can be seen as a relaxation of the classical notion of connected component when going from C([omega]) to WBV([omega])
A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification
Action spotting in soccer videos is the task of identifying the specific time
when a certain key action of the game occurs. Lately, it has received a large
amount of attention and powerful methods have been introduced. Action spotting
involves understanding the dynamics of the game, the complexity of events, and
the variation of video sequences. Most approaches have focused on the latter,
given that their models exploit the global visual features of the sequences. In
this work, we focus on the former by (a) identifying and representing the
players, referees, and goalkeepers as nodes in a graph, and by (b) modeling
their temporal interactions as sequences of graphs. For the player
identification, or player classification task, we obtain an accuracy of 97.72%
in our annotated benchmark. For the action spotting task, our method obtains an
overall performance of 57.83% average-mAP by combining it with other
audiovisual modalities. This performance surpasses similar graph-based methods
and has competitive results with heavy computing methods. Code and data are
available at https://github.com/IPCV/soccer_action_spotting.Comment: Accepted at the 5th International ACM Workshop on Multimedia Content
Analysis in Sports (MMSports 2022
ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution
The colorization of grayscale images is an ill-posed problem, with multiple
correct solutions. In this paper, we propose an adversarial learning
colorization approach coupled with semantic information. A generative network
is used to infer the chromaticity of a given grayscale image conditioned to
semantic clues. This network is framed in an adversarial model that learns to
colorize by incorporating perceptual and semantic understanding of color and
class distributions. The model is trained via a fully self-supervised strategy.
Qualitative and quantitative results show the capacity of the proposed method
to colorize images in a realistic way achieving state-of-the-art results.Comment: 8 pages + reference
Multi-Person Tracking By Multi-Scale Detection in Basketball Scenarios
Tracking data is a powerful tool for basketball teams in order to extract advanced semantic information and statistics that might lead to a performance boost. However, multi-person tracking is a challenging task to solve in single-camera video sequences, given the frequent occlusions and cluttering that occur in a restricted scenario. In this paper, a novel multi-scale detection method is presented, which is later used to extract geometric and content features, resulting in a multi-person video tracking system. Having built a dataset from scratch together with its ground truth (more than 10k bounding boxes), standard metrics are evaluated, obtaining notable results both in terms of detection (F1-score) and tracking (MOTA). The presented system could be used as a source of data gathering in order to extract useful statistics and semantic analyses a posteriori
Using Player's Body-Orientation to Model Pass Feasibility in Soccer
Given a monocular video of a soccer match, this paper presents a
computational model to estimate the most feasible pass at any given time. The
method leverages offensive player's orientation (plus their location) and
opponents' spatial configuration to compute the feasibility of pass events
within players of the same team. Orientation data is gathered from body pose
estimations that are properly projected onto the 2D game field; moreover, a
geometrical solution is provided, through the definition of a feasibility
measure, to determine which players are better oriented towards each other.
Once analyzed more than 6000 pass events, results show that, by including
orientation as a feasibility measure, a robust computational model can be
built, reaching more than 0.7 Top-3 accuracy. Finally, the combination of the
orientation feasibility measure with the recently introduced Expected
Possession Value metric is studied; promising results are obtained, thus
showing that existing models can be refined by using orientation as a key
feature. These models could help both coaches and analysts to have a better
understanding of the game and to improve the players' decision-making process.Comment: Accepted at the Computer Vision in Sports Workshop at CVPR 202
Using player's body-orientation to model pass feasibility in soccer
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Given a monocular video of a soccer match, this paper presents a computational model to estimate the most feasible pass at any given time. The method leverages offensive player's orientation (plus their location) and opponents' spatial configuration to compute the feasibility of pass events within players of the same team. Orientation data is gathered from body pose estimations that are properly projected onto the 2D game field; moreover, a geometrical solution is provided, through the definition of a feasibility measure, to determine which players are better oriented towards each other. Once analyzed more than 6000 pass events, results show that, by including orientation as a feasibility measure, a robust computational model can be built, reaching more than 0.7 Top-3 accuracy. Finally, the combination of the orientation feasibility measure with the recently introduced Expected Possession Value metric is studied; promising results are obtained, thus showing that existing models can be refined by using orientation as a key feature. These models could help both coaches and analysts to have a better understanding of the game and to improve the players' decision-making process.Peer ReviewedPostprint (author's final draft
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