Sport Players Detection and Tracking With a Mixed Network of Planar and Omnidirectional Cameras

A generic approach is presented to detect and track people with a network of fixed and omnidirectional cameras given severely degraded foreground silhouettes. The problem is formulated as a sparsity constrained inverse problem. A dictionary made of atoms representing the presence of a person at a given location is used within the problem formulation. A re- weighted scheme is considered to better approximate the sparsity prior. Although the framework is generic to any scene, the focus of this paper is to evaluate the strength of the proposed approach on a basketball game. The main challenges come from the players' behavior, their similar appearance, and the mutual occlusions present in the views. In addition, the extracted foreground silhouettes are severely degraded due to the polished floor reflecting the players, and the strong shadow present in the scene. We present qualitative and quantitative results with the APIDIS dataset as part of the ICDSC sport challenge

Automated classification of cricket pitch frames in cricket video

The automated detection of the cricket pitch in a video recording of a cricket match is a fundamental step in content-based indexing and summarization of cricket videos. In this paper, we propose visualcontent based algorithms to automate the extraction of video frames with the cricket pitch in focus. As a preprocessing step, we first select a subset of frames with a view of the cricket field, of which the cricket pitch forms a part. This filtering process reduces the search space by eliminating frames that contain a view of the audience, close-up shots of specific players, advertisements, etc. The subset of frames containing the cricket field is then subject to statistical modeling of the grayscale (brightness) histogram (SMoG). Since SMoG does not utilize color or domain-specific information such as the region in the frame where the pitch is expected to be located, we propose an alternative algorithm: component quantization based region of interest extraction (CQRE) for the extraction of pitch frames. Experimental results demonstrate that, regardless of the quality of the input, successive application of the two methods outperforms either one applied exclusively. The SMoG-CQRE combination for pitch frame classification yields an average accuracy of 98:6% in the best case (a high resolution video with good contrast) and an average accuracy of 87:9% in the worst case (a low resolution video with poor contrast). Since, the extraction of pitch frames forms the first step in analyzing the important events in a match, we also present a post-processing step, viz. , an algorithm to detect players in the extracted pitch frames

Dynamical Stability and Predictability of Football Players: The Study of One Match

The game of football demands new computational approaches to measure individual and collective performance. Understanding the phenomena involved in the game may foster the identification of strengths and weaknesses, not only of each player, but also of the whole team. The development of assertive quantitative methodologies constitutes a key element in sports training. In football, the predictability and stability inherent in the motion of a given player may be seen as one of the most important concepts to fully characterise the variability of the whole team. This paper characterises the predictability and stability levels of players during an official football match. A Fractional Calculus (FC) approach to define a player’s trajectory. By applying FC, one can benefit from newly considered modeling perspectives, such as the fractional coefficient, to estimate a player’s predictability and stability. This paper also formulates the concept of attraction domain, related to the tactical region of each player, inspired by stability theory principles. To compare the variability inherent in the player’s process variables (e.g., distance covered) and to assess his predictability and stability, entropy measures are considered. Experimental results suggest that the most predictable player is the goalkeeper while, conversely, the most unpredictable players are the midfielders. We also conclude that, despite his predictability, the goalkeeper is the most unstable player, while lateral defenders are the most stable during the match

Deep labeller: automatic bounding box generation for synthetic violence detection datasets

Manually labelling datasets for training violence detection systems is time-consuming, expensive, and labor-intensive. Mind wandering, boredom, and short attention span can also cause labelling errors. Moreover, collecting and distributing sensitive images containing violence has ethical implications. Automation is the future for labelling sensitive image datasets. Deep labeller is a two-stage Deep Learning (DL) method that uses pre-trained DL object detection methods on MS-COCO for automatic labelling. The Deep Labeller method labels violent and nonviolent images in WVD and USI. In stage 1, WVD generates weak labels using synthetic images. In stage 2, the Deep labeller method is retrained on weak labels. USI dataset is used to test our method on real-world violence. Deep labeller generated weak and strong labels with an IoU of 0.80036 in stage 1 and 0.95 in stage 2 on the WVD. Automatically generated labels. To test our method’s generalisation power, violent and nonviolent image labels on USI dataset had a mean IoU of 0.7450

Challenges and considerations in determining the quality of electronic performance & tracking systems for team sports

Electronic performance & tracking systems (EPTS) are commonly used to track the location and velocity of athletes in many team sports. A range of associated applications using the derived data exist, such as assessment of athlete characteristics, informing training design, assisting match adjudication and providing fan insights for broadcast. Consequently the quality of such systems is of importance to a range of stakeholders. The influence of both systematic and methodological factors such as hardware, software settings, sample rate and filtering on this resulting quality is non-trivial. Highlighting these allows for the user to understand their strengths and limitations in various decision-making processes, as well as identify areas for research and development. In this paper, a number of challenges and considerations relating to the determination of EPTS validity for team sport are outlined and discussed. The aim of this paper is to draw attention of these factors to both researchers and practitioners looking to inform their decision-making in the EPTS area. Addressing some of the posited considerations in future work may represent best practice; others may require further investigation, have multiple potential solutions or currently be intractable

Challenges and considerations in determining the quality of electronic performance & tracking systems for team sports

Electronic performance & tracking systems (EPTS) are commonly used to track the location and velocity of athletes in many team sports. A range of associated applications using the derived data exist, such as assessment of athlete characteristics, informing training design, assisting match adjudication and providing fan insights for broadcast. Consequently the quality of such systems is of importance to a range of stakeholders. The influence of both systematic and methodological factors such as hardware, software settings, sample rate and filtering on this resulting quality is non-trivial. Highlighting these allows for the user to understand their strengths and limitations in various decision-making processes, as well as identify areas for research and development. In this paper, a number of challenges and considerations relating to the determination of EPTS validity for team sport are outlined and discussed. The aim of this paper is to draw attention of these factors to both researchers and practitioners looking to inform their decision-making in the EPTS area. Addressing some of the posited considerations in future work may represent best practice; others may require further investigation, have multiple potential solutions or currently be intractable

A Novel and Effective Short Track Speed Skating Tracking System

This dissertation proposes a novel and effective system for tracking high-speed skaters. A novel registration method is employed to automatically discover key frames to build the panorama. Then, the homography between a frame and the real world rink can be generated accordingly. Aimed at several challenging tracking problems of short track skating, a novel multiple-objects tracking approach is proposed which includes: Gaussian mixture models (GMMs), evolving templates, constrained dynamical model, fuzzy model, multiple templates initialization, and evolution. The outputs of the system include spatialtemporal trajectories, velocity analysis, and 2D reconstruction animations. The tracking accuracy is about 10 cm (2 pixels). Such information is invaluable for sports experts. Experimental results demonstrate the effectiveness and robustness of the proposed system