Supporting activity recognition by visual analytics

Recognizing activities has become increasingly relevant in many application domains, such as security or ambient assisted living. To handle different scenarios, the underlying automated algorithms are configured using multiple input parameters. However, the influence and interplay of these parameter...

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Vydáno v:2015 IEEE Conference on Visual Analytics Science and Technology (VAST) s. 41 - 48
Hlavní autoři: Rohlig, Martin, Luboschik, Martin, Kruger, Frank, Kirste, Thomas, Schumann, Heidrun, Bogl, Markus, Alsallakh, Bilal, Miksch, Silvia
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2015
Témata:
Algorithm design and analysis
Data visualization
H.5.2 [Information Interfaces and Presentation]: User Interfaces—Theory and methods
I.3.6 [Computing Methodologies]: Computer Graphics—Methodology and Techniques
Prediction algorithms
Statistical analysis
Time series analysis
Visual analytics
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Abstract Recognizing activities has become increasingly relevant in many application domains, such as security or ambient assisted living. To handle different scenarios, the underlying automated algorithms are configured using multiple input parameters. However, the influence and interplay of these parameters is often not clear, making exhaustive evaluations necessary. On this account, we propose a visual analytics approach to supporting users in understanding the complex relationships among parameters, recognized activities, and associated accuracies. First, representative parameter settings are determined. Then, the respective output is computed and statistically analyzed to assess parameters' influence in general. Finally, visualizing the parameter settings along with the activities provides overview and allows to investigate the computed results in detail. Coordinated interaction helps to explore dependencies, compare different settings, and examine individual activities. By integrating automated, visual, and interactive means users can select parameter values that meet desired quality criteria. We demonstrate the application of our solution in a use case with realistic complexity, involving a study of human protagonists in daily living with respect to hundreds of parameter settings.
AbstractList Recognizing activities has become increasingly relevant in many application domains, such as security or ambient assisted living. To handle different scenarios, the underlying automated algorithms are configured using multiple input parameters. However, the influence and interplay of these parameters is often not clear, making exhaustive evaluations necessary. On this account, we propose a visual analytics approach to supporting users in understanding the complex relationships among parameters, recognized activities, and associated accuracies. First, representative parameter settings are determined. Then, the respective output is computed and statistically analyzed to assess parameters' influence in general. Finally, visualizing the parameter settings along with the activities provides overview and allows to investigate the computed results in detail. Coordinated interaction helps to explore dependencies, compare different settings, and examine individual activities. By integrating automated, visual, and interactive means users can select parameter values that meet desired quality criteria. We demonstrate the application of our solution in a use case with realistic complexity, involving a study of human protagonists in daily living with respect to hundreds of parameter settings.
Author Alsallakh, Bilal
Rohlig, Martin
Kruger, Frank
Luboschik, Martin
Schumann, Heidrun
Kirste, Thomas
Miksch, Silvia
Bogl, Markus
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  surname: Rohlig
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  organization: Vienna Univ. of Technol., Vienna, Austria
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Snippet Recognizing activities has become increasingly relevant in many application domains, such as security or ambient assisted living. To handle different...
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SubjectTerms Algorithm design and analysis
Data visualization
H.5.2 [Information Interfaces and Presentation]: User Interfaces—Theory and methods
I.3.6 [Computing Methodologies]: Computer Graphics—Methodology and Techniques
Prediction algorithms
Statistical analysis
Time series analysis
Visual analytics
Title Supporting activity recognition by visual analytics
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