Automatic registration of broadcast soccer images to a standardized field model enables advanced analytics, augmented reality overlays, and precise player tracking. We propose a fully automatic, viewpoint-independent homography estimation pipeline fusing three complementary geometric cues: white field markings (lines and elliptical arcs), grass-band delimitations, and a binary playing-field mask. Detected primitives are first richly labeled — classifying lines as longitudinal or transversal, characterizing grass-tone transitions, and encoding four-quadrant intersection patterns — to reduce correspondence ambiguity. We then generate and prune candidate subsets of primitives, establish plausible matches to model elements via intersection-pattern rules and projective cross-ratio invariants, and systematically evaluate homography hypotheses using bidirectional mask-projection accuracies and mean reprojection error. An experimental evaluation on the LaSoDa benchmark demonstrates that the proposed method achieves highly accurate registrations with ground-truth primitives and robust performance in the fully automatic end-to-end pipeline. Furthermore, comparative experiments with recent state-of-the-art approaches confirm improved precision and robustness across diverse broadcast scenarios.

This study investigates the use of an innovative educational tool, a video coding app, to improve students’ understanding of video coding concepts. The app was implemented in a university-level computer science course, and data were collected on students’ perceptions of the tool and their performance on knowledge tests. In this study, 7 participants conducted two practical sessions using the app. In each session, they performed a pre- and post-test evaluation. After each session, they also completed two subjective questionnaires to measure the perception of learning and usability of the tool. Results showed that the app was perceived as engaging and easy to use by students. However, analysis of test performance did not show a significant impact on learning outcomes. This suggests that the quality of experience may not always be correlated with objective results when evaluating new learning tools. As a lesson learned, we always recommend using pilot studies when evaluating the performance of new teaching tools. In addition, such studies should subjectively and objectively measure the influence of the tool on student performance.

Abstract We propose a novel strategy for the automatic detection of highlight events in user-generated tricking videos, to the best of our knowledge, the first one specifically tailored for this complex sport. Most current methods for related sports leverage high-level semantics such as predefined camera angles or common editing practices, or rely on depth cameras to achieve automatic detection. However, our approach only relies on the contents (themselves) in the frames of a given video, and consists in a four stage pipeline. The first stage identifies foreground key points of interest along with an estimation of their motion in the video frames. In the second stage, these points are grouped into regions of interest based on their proximity and motion. Their behavior over time is evaluated in the third stage to generate an attention map indicating the regions participating in the most relevant events. The fourth and final stage provides the extracted video sequences where highlights have been identified. Experimental results attest to the effectiveness of our approach, which shows high recall and precision values at frame level, with detections that fit well the ground truth events.

Augmented reality applications are beginning to change the way sports are broadcast, providing richer experiences and valuable insights to fans. The first step of augmented reality systems is camera calibration, possibly based on detecting the line markings of the playing field. Most existing proposals for line detection rely on edge detection and Hough transform, but radial distortion and extraneous edges cause inaccurate or spurious detections of line markings. We propose a novel strategy to automatically and accurately segment and classify line markings. First, line points are segmented thanks to a stochastic watershed transform that is robust to radial distortions, since it makes no assumptions about line straightness, and is unaffected by the presence of players or the ball. The line points are then linked to primitive structures (straight lines and ellipses) thanks to a very efficient procedure that makes no assumptions about the number of primitives that appear in each image. The strategy has been tested on a new and public database composed by 60 annotated images from matches in five stadiums. The results obtained have proven that the proposed strategy is more robust and accurate than existing approaches, achieving successful line mark detection even under challenging conditions.

We present a novel dataset for the evaluation of face detection and recognition algorithms in challenging surveillance scenarios. The dataset consists in 4K images of different subjects captured at annotated distances ranging from 1 to 30 meters, both in indoor and outdoor environments, and under two face mask conditions (with and without). To the best of our knowledge, this is the only existing dataset that addresses the joint impact of masks and distances in a rigorous manner. We also propose an end-to-end fully automatic face detection and recognition system to provide baseline results on this dataset. Face detection is performed using Tiny Faces network, while face recognition is performed using VGG Face network. Experimental results show very high detection and recognition rates up to a distance of 20 meters, where the impact of distance is clear (especially for the latter). The use of face masks degrades the detection range and produces less consistent recognition results.

The registration of images of soccer matches is a key stage in many computer vision applications. Until now, this task has been typically carried out from key points obtained from the white line marks drawn on the field of play, but in many cases this does not yield enough keypoints for a robust registration. This article proposes a strategy to detect the borders between the grass bands of the field of play and therefore makes it possible to locate many more key points that will allow to carry out a subsequent registration of the images. First, a preprocessing is applied to obtain a grayscale image in which the grass bands are easily distinguishable, and also to obtain a binary mask of the entire field of play that determines the area of interest. Then, a local analysis is carried out to detect most of the borders between grass bands. Finally, a global analysis based on the intersections between lines is applied to group the detected borders and rule out false detections. The strategy has been evaluated on two databases composed of hundreds of annotated images from matches in several stadiums with different characteristics and light conditions. The results obtained have shown that most of the lines delimiting the grass bands are found successfully, while the number of false detections is very small.

There is a huge proliferation of surveillance systems that require strategies for detecting different kinds of stationary foreground objects (e.g., unattended packages or illegally parked vehicles). As these strategies must be able to detect foreground objects remaining static in crowd scenarios, regardless of how long they have not been moving, several algorithms for detecting different kinds of such foreground objects have been developed over the last decades. This paper presents an efficient and high-quality strategy to detect stationary foreground objects, which is able to detect not only completely static objects but also partially static ones. Three parallel nonparametric detectors with different absorption rates are used to detect currently moving foreground objects, short-term stationary foreground objects, and long-term stationary foreground objects. The results of the detectors are fed into a novel finite state machine that classifies the pixels among background, moving foreground objects, stationary foreground objects, occluded stationary foreground objects, and uncovered background. Results show that the proposed detection strategy is not only able to achieve high quality in several challenging situations but it also improves upon previous strategies.

Many computer vision and human-computer interaction applications developed in recent years need evaluating complex and continuous mathematical functions as an essential step toward proper operation. However, rigorous evaluation of these kind of functions often implies a very high computational cost, unacceptable in real-time applications. To alleviate this problem, functions are commonly approximated by simpler piecewise-polynomial representations. Following this idea, we propose a novel, efficient, and practical technique to evaluate complex and continuous functions using a nearly optimal design of two types of piecewise linear approximations in the case of a large budget of evaluation subintervals. To this end, we develop a thorough error analysis that yields asymptotically tight bounds to accurately quantify the approximation performance of both representations. It provides an improvement upon previous error estimates and allows the user to control the tradeoff between the approximation error and the number of evaluation subintervals. To guarantee real-time operation, the method is suitable for, but not limited to, an efficient implementation in modern graphics processing units, where it outperforms previous alternative approaches by exploiting the fixed-function interpolation routines present in their texture units. The proposed technique is a perfect match for any application requiring the evaluation of continuous functions; we have measured in detail its quality and efficiency on several functions, and, in particular, the Gaussian function because it is extensively used in many areas of computer vision and cybernetics, and it is expensive to evaluate.

Image segmentation is a fundamental step in many image processing applications. To achieve high-quality segmentations active contours are commonly used. However, state of art strategies are not able to provide successful results in all the conditions. Additionally, the strategies that get the best overall results are computationally expensive and need to manually set some parameters, which decreases their usability. Here, we propose a novel active contours-based segmentation method that, through the combination of boundary-based and region-based energies and a multiresolution analysis, provides very high-quality results while significantly increasing both the computational efficiency and the usability of previous approaches.

Recently, the number of electronic devices with smart cameras has grown enormously. These devices require new, fast, and efficient computer vision applications that include moving object detection strategies. In this paper, a novel and high-quality strategy for real-time moving object detection by nonparametric modeling is presented. It is suitable for its application to smart cameras operating in real time in a large variety of scenarios. While the background is modeled using an innovative combination of chromaticity and gradients, reducing the influence of shadows and reflected light in the detections, the foreground model combines this information and spatial information. The application of a particle filter allows to update the spatial information and provides a priori knowledge about the areas to analyze in the following images, enabling an important reduction in the computational requirements and improving the segmentation results. The quality of the results and the achieved computational efficiency show the suitability of the proposed strategy to enable new applications and opportunities in last generation of electronic devices.

Along the recent years, several moving object detection strategies by non-parametric background-foreground modeling have been proposed. To combine both models and to obtain the probability of a pixel to belong to the foreground, these strategies make use of Bayesian classifiers. However, these classifiers do not allow to take advantage of additional prior information at different pixels. So, we propose a novel and efficient alternative Bayesian classifier that is suitable for this kind of strategies and that allows the use of whatever prior information. Additionally, we present an effective method to dynamically estimate prior probability from the result of a particle filter-based tracking strategy.

Here, a novel and efficient moving object detection strategy by non-parametric modeling is presented. Whereas the foreground is modeled by combining color and spatial information, the background model is constructed exclusively with color information, thus resulting in a great reduction of the computational and memory requirements. The estimation of the background and foreground covariance matrices, allows us to obtain compact moving regions while the number of false detections is reduced. Additionally, the application of a tracking strategy provides a priori knowledge about the spatial position of the moving objects, which improves the performance of the Bayesian classifier.

This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy applied over previously detected foreground regions allows to construct a robust foreground modeling, which combined with the background model increases noticeably the quality in the detections. The proposed strategy has been applied to different kind of sequences, obtaining satisfactory results in complex situations such as those given by dynamic backgrounds, illumination changes, shadows and multiple moving objects.

Multiple object tracking is a main research area in the computer vision field. Particle filters have shown their performance as a powerful tool allowing to track visual objects giving temporal coherence to incoming observations, as well as offering an excellent framework for this task due to its inherent multimodality. However, traditional algorithms for particle filters do not cope directly with multiple objects and several considerations have to be addressed. In this work, an efficient reclustering strategy is proposed, which takes into account new measurements according to a novelty function, and provides a criterium to determine the minimum required number of particles to be drawn for each tracked object. To show its performance, this strategy has been used as a multiple 2D object tracking for video-surveillance applications. Excellent results are obtained, in terms of efficiency and accuracy.