Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. © 1995-2012 IEEE

Hierarchical event selection for video storyboards with a case study on snooker video visualization

Video storyboard, which is a form of video visualization, summarizes the major events in a video using illustrative visualization. There are three main technical challenges in creating a video storyboard, (a) event classification, (b) event selection and (c) event illustration. Among these challenges, (a) is highly application-dependent and requires a significant amount of application specific semantics to be encoded in a system or manually specified by users. This paper focuses on challenges (b) and (c). In particular, we present a framework for hierarchical event representation, and an importance-based selection algorithm for supporting the creation of a video storyboard from a video. We consider the storyboard to be an event summarization for the whole video, whilst each individual illustration on the board is also an event summarization but for a smaller time window. We utilized a 3D visualization template for depicting and annotating events in illustrations. To demonstrate the concepts and algorithms developed, we use Snooker video visualization as a case study, because it has a concrete and agreeable set of semantic definitions for events and can make use of existing techniques of event detection and 3D reconstruction in a reliable manner. Nevertheless, most of our concepts and algorithms developed for challenges (b) and (c) can be applied to other application areas. © 2010 IEEE

Knowledge-assisted ranking: A visual analytic application for sports event data

© 2016 IEEE. Organizing sports video data for performance analysis can be challenging, especially in cases involving multiple attributes and when the criteria for sorting frequently changes depending on the user's task. The proposed visual analytic system enables users to specify a sort requirement in a flexible manner without depending on specific knowledge about individual sort keys. The authors use regression techniques to train different analytical models for different types of sorting requirements and use visualization to facilitate knowledge discovery at different stages of the process. They demonstrate the system with a rugby case study to find key instances for analyzing team and player performance. Organizing sports video data for performance analysis can be challenging in cases with multiple attributes, and when sorting frequently changes depending on the user's task. As this video shows, the proposed visual analytic system allows interactive data sorting and exploration

Mass enhances speed but diminishes turn capacity in terrestrial pursuit predators

The dynamics of predator-prey pursuit appears complex, making the development of a framework explaining predator and prey strategies problematic. We develop a model for terrestrial, cursorial predators to examine how animal mass modulates predator and prey trajectories and affects best strategies for both parties. We incorporated the maximum speed-mass relationship with an explanation of why larger animals should have greater turn radii; the forces needed to turn scale linearly with mass whereas the maximum forces an animal can exert scale to a 2/3 power law. This clarifies why in a meta-analysis, we found a preponderance of predator/prey mass ratios that minimized the turn radii of predators compared to their prey. It also explained why acceleration data from wild cheetahs pursuing different prey showed different cornering behaviour with prey type. The outcome of predator prey pursuits thus depends critically on mass effects and the ability of animals to time turns precisely.Royal Society (2009/R3 JP090604) and NERC (NE/I002030/1). SANParks and the Department of Wildlife and National Parks, Botswana for allowing our research in the KgalagadiTransfrontier Park (Permit Number 2006-05-01 MGLM) and from The Lewis Foundation, South Africa, The Howard G Buffet Foundation, National Geographic, Kanabo Conservation Link, Comanis Foundation, Panthera and the Kruger Park Marathon Club.http://elifesciences.orghb201

Glyph sorting: Interactive visualization for multi-dimensional data

Copyright © 2013 The Author(s). Glyph-based visualization is an effective tool for depicting multivariate information. Since sorting is one of the most common analytical tasks performed on individual attributes of a multi-dimensional dataset, this motivates the hypothesis that introducing glyph sorting would significantly enhance the usability of glyph-based visualization. In this article, we present a glyph-based conceptual framework as part of a visualization process for interactive sorting of multivariate data. We examine several technical aspects of glyph sorting and provide design principles for developing effective, visually sortable glyphs. Glyphs that are visually sortable provide two key benefits: (1) performing comparative analysis of multiple attributes between glyphs and (2) to support multi-dimensional visual search. We describe a system that incorporates focus and context glyphs to control sorting in a visually intuitive manner and for viewing sorted results in an interactive, multidimensional glyph plot that enables users to perform high-dimensional sorting, analyse and examine data trends in detail. To demonstrate the usability of glyph sorting, we present a case study in rugby event analysis for comparing and analysing trends within matches. This work is undertaken in conjunction with a national rugby team. From using glyph sorting, analysts have reported the discovery of new insight beyond traditional match analysis

Systematic Snooker Skills Test to Analyze Player Performance

The process of rigorous training and coaching is one that is essential to any sports player aiming to develop their abilities further. From the novice player through to professional athletes, it is vital to maintain and assess their level of performance in order to progress to a higher standard. However, traditional practice routines can often be non-strategic and devised with an “ad-hoc” approach. In order for a training regime to be beneficial to a player, methods to examine a player's performance are desirable and can offer quantifiable feedback that will help the player to understand their current weaknesses and provide a benchmark to improve upon. This article focuses on the introduction of a systematic skills test. We assess the fundamental physics of snooker and from this we identify a set of key skills that characterises the basis of all snooker shots. We present 5 snooker tests that can be used to quantify the performance of these key skills. This allows us to analyse snooker players in an objective manner based on their level of ability for each key skill. The article concludes with a user study that assesses the performance of novice, intermediate and professional players when performing our proposed snooker skills test, which demonstrates the ability to make accurate comparison between players of different ability

Intelligent filtering by semantic importance for single-view 3d reconstruction from snooker video

In this paper we investigate the challenge of 3D reconstruction from Snooker video data. We propose a system pipeline for intelligent filtering based on semantic importance in Snooker. The system can be divided into table detection and correction, followed by ball detection, classification and tracking. It is apparent from previous work that there are several challenges presented here. Firstly, previous methods tend to use a fixed top-down camera mounted above the table. To capture a full table view from this is challenging due to space limitations above the table. Instead, we capture video data from a tripod and correct the viewpoint through processing. Secondly, previous methods tend to simply detect the balls without considering other interfering objects such as player and cue. This becomes even more apparent when the player strikes the cue ball. Our intelligent filtering avoids such issues to give accurate 3D table reconstruction

From Video to Animated 3D Reconstruction: A Computer Graphics Application for Snooker Skills Training.

This poster will present a computer graphics application for improving snooker skills training. We developed an automated modelling and rendering pipeline that converts video input data to a time-varying 3D graphical model that can be animated from arbitrary viewing positions. In addition, we introduced illustrative rendering capability that provides coaches and players with various annotated graphics as training aids. The reconstruction of 3D models relies only on a single camera view