Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop

Legg, Phil; Chung, David; Parry, Matthew L.; Bown, Rhodri; Jones, Mark W.; Griffiths, Iwan W.; Chen, Min

doi:10.1109/tvcg.2013.207

Cited by 26 publications

(19 citation statements)

References 36 publications

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“…Paper [27] describes application of interactive video analysis to sport. After extracting trajectories of players from a video of a rugby game, the proposed system supports queries based on sketches.…”

Section: Visualization For Video Analyticsmentioning

confidence: 99%

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Stein

Janetzko

Lamprecht

et al. 2018

IEEE Trans. Visual. Comput. Graphics

130

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach. Permanent repository link

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Section: Visualization For Video Analyticsmentioning

confidence: 99%

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Stein

Janetzko

Lamprecht

et al. 2018

IEEE Trans. Visual. Comput. Graphics

130

View full text Add to dashboard Cite

show abstract

“…Some systems aim at supporting understanding of several mining procedures [3], [18], [23]. The iVisClassifier [4], that employs linear discriminant analysis (LDA) to perform a dimensionality reduction, focuses on group discriminability in order to ease the labeling of new instances.…”

Section: Visual Data Classificationmentioning

confidence: 99%

“…In [23], the authors highlight the difficulty, in the image and video search scenarios, of adequately annotating instances, as well as the lack of suitable training data. They have developed a visual analytics system that gives visual feedback to users, along with a normalized maneuver visualizations to explore the video data.…”

Section: Visual Data Classificationmentioning

confidence: 99%

An Approach to Supporting Incremental Visual Data Classification

Paiva

Schwartz

Pedrini

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Abstract-Automatic data classification is a computationally intensive task that presents variable precision and is considerably sensitive to the classifier configuration and to data representation, particularly for evolving data sets. Some of these issues can best be handled by methods that support users' control over the classification steps. In this paper, we propose a visual data classification methodology that supports users in tasks related to categorization such as training set selection; model creation, application and verification; and classifier tuning. The approach is then well suited for incremental classification, present in many applications with evolving data sets. Data set visualization is accomplished by means of point placement strategies, and we exemplify the method through multidimensional projections and Neighbor Joining trees. The same methodology can be employed by a user who wishes to create his or her own ground truth (or perspective) from a previously unlabeled data set. We validate the methodology through its application to categorization scenarios of image and text data sets, involving the creation, application, verification, and adjustment of classification models.

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“…With this, the visual analytics provides model visualization that enables the analyst to understand how the underlying detection routine is configured, and to assess how effective this configuration may be. Model visualization is becoming an integral part of many visual analytic tools [16,25], enabling the analyst to better understand the machine learning or data transformation processes that are applied. Here, we incorporate an interactive PCA approach proposed by Jeong et al [10] to highlight the relationship between the PCA space and the original feature space.…”

Section: Visual Analytics Systemmentioning

confidence: 99%

Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop

Cited by 26 publications

References 36 publications

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis

An Approach to Supporting Incremental Visual Data Classification

Visualizing the insider threat: challenges and tools for identifying malicious user activity

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