Automated Top View Registration of Broadcast Football Videos

In this paper, we propose a novel method to register football broadcast video frames on the static top view model of the playing surface. The proposed method is fully automatic in contrast to the current state of the art which requires manual initialization of point correspondences between the image and the static model. Automatic registration using existing approaches has been difficult due to the lack of sufficient point correspondences. We investigate an alternate approach exploiting the edge information from the line markings on the field. We formulate the registration problem as a nearest neighbour search over a synthetically generated dictionary of edge map and homography pairs. The synthetic dictionary generation allows us to exhaustively cover a wide variety of camera angles and positions and reduce this problem to a minimal per-frame edge map matching procedure. We show that the per-frame results can be improved in videos using an optimization framework for temporal camera stabilization. We demonstrate the efficacy of our approach by presenting extensive results on a dataset collected from matches of football World Cup 2014

A Tool for Annotating Homographies from Hockey Broadcast Video

In order to develop solutions for automatic ice rink localization from broadcast video, a dataset with ground truth homographies is required. Hockey broadcast video does not tend to provide camera parameters for each frame, which means that they must be gathered manually. A novel tool for collecting ground truth transforms through point correspondences between each frame and an overhead view of the ice rink is presented in this paper. Through collaboration with the users of the tool, we have added features to improve accuracy and efficiency, especially in frames with few lines on the playing surface visible. A dataset of 4,262 frames has been collected, which will be used for research into automatic camera calibration techniques

Sports Field Localization using Memory Networks

Sports analytics derived automatically from broadcast footage is agrowing interest because it provides advantageous data to teamswithout the need for specialized equipment or trained staff. A fundamental step in automating sports video analytics extraction is registering the playing surface and transforming the broadcast footageto a top-down view. In this paper, a novel method is presentedthat performs automatic top-down registration of sports fields using temporal information. Using richer input data will increase the performance of the network and will not require an additional correctionnetwork

Play type recognition in real-world football video

This paper presents a vision system for recognizing the sequence of plays in amateur videos of American football games (e.g. offense, defense, kickoff, punt, etc). The sys-tem is aimed at reducing user effort in annotating foot-ball videos, which are posted on a web service used by over 13,000 high school, college, and professional football teams. Recognizing football plays is particularly challeng-ing in the context of such a web service, due to the huge variations across videos, in terms of camera viewpoint, mo-tion, distance from the field, as well as amateur camerawork quality, and lighting conditions, among other factors. Given a sequence of videos, where each shows a particular play of a football game, we first run noisy play-level detectors on every video. Then, we integrate responses of the play-level detectors with global game-level reasoning which accounts for statistical knowledge about football games. Our empir-ical results on more than 1450 videos from 10 diverse foot-ball games show that our approach is quite effective, and close to being usable in a real-world setting. 1

Sports Camera Pose Refinement Using an Evolution Strategy

This paper presents a robust end-to-end method for sports cameras extrinsic parameters optimization using a novel evolution strategy. First, we developed a neural network architecture for an edge or area-based segmentation of a sports field. Secondly, we implemented the evolution strategy, which purpose is to refine extrinsic camera parameters given a single, segmented sports field image. Experimental comparison with state-of-the-art camera pose refinement methods on real-world data demonstrates the superiority of the proposed algorithm. We also perform an ablation study and propose a way to generalize the method to additionally refine the intrinsic camera matrix.Comment: Conference paper at 2022 IEEE Congress on Evolutionary Computation (CEC

Precise video feedback through live annotation of football

The domain of sports analysis is a huge field in sports science. Several different computer systems are available for doing analysis, both expensive and less expensive. Some specialize in specific sports such as football or ice hockey, while others are sports agnostic. However, a common property of most of these systems is that they try to give in-depth and detailed analysis of the sport in question. This thesis proposes and describes a system that provides the user with the ability to annotate interesting happenings during a live sporting event, through a non-invasive mobile device interface. The device permits focus on important happenings by filtering out unnecessary detail. Our system provides corresponding video of the annotations on the same mobile device, thereby facilitating the process of giving video feedback to the involved coaches and players. We have implemented a prototype of the system that enables evaluation of this idea, and through case studies with Tromsø Idrettslag, a Norwegian Premier League football club, we show its usefulness and applicability

Homography Estimation in Complex Topological Scenes

Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection. Extrinsic camera calibration data is important for most analysis applications. However, security cameras are susceptible to environmental conditions and small camera movements, resulting in a need for an automated re-calibration method that can account for these varying conditions. In this paper, we present an automated camera-calibration process leveraging a dictionary-based approach that does not require prior knowledge on any camera settings. The method consists of a custom implementation of a Spatial Transformer Network (STN) and a novel topological loss function. Experiments reveal that the proposed method improves the IoU metric by up to 12% w.r.t. a state-of-the-art model across five synthetic datasets and the World Cup 2014 dataset.Comment: Will be published in Intelligent Vehicle Symposium 202