A results-based process for evaluation of diverse visual analytics tools

Gary Rubin; David H. Berger

doi:10.1117/12.2015396

16 May 2013 A results-based process for evaluation of diverse visual analytics tools

Gary Rubin, David H. Berger

Proceedings Volume 8740, Motion Imagery Technologies, Best Practices, and Workflows for Intelligence, Surveillance, and Reconnaissance (ISR), and Situational Awareness; 87400B (2013) https://doi.org/10.1117/12.2015396
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

With the pervasiveness of still and full-motion imagery in commercial and military applications, the need to ingest and analyze these media has grown rapidly in recent years. Additionally, video hosting and live camera websites provide a near real-time view of our changing world with unprecedented spatial coverage. To take advantage of these controlled and crowd-sourced opportunities, sophisticated visual analytics (VA) tools are required to accurately and efficiently convert raw imagery into usable information. Whether investing in VA products or evaluating algorithms for potential development, it is important for stakeholders to understand the capabilities and limitations of visual analytics tools. Visual analytics algorithms are being applied to problems related to Intelligence, Surveillance, and Reconnaissance (ISR), facility security, and public safety monitoring, to name a few. The diversity of requirements means that a onesize- fits-all approach to performance assessment will not work. We present a process for evaluating the efficacy of algorithms in real-world conditions, thereby allowing users and developers of video analytics software to understand software capabilities and identify potential shortcomings. The results-based approach described in this paper uses an analysis of end-user requirements and Concept of Operations (CONOPS) to define Measures of Effectiveness (MOEs), test data requirements, and evaluation strategies. We define metrics that individually do not fully characterize a system, but when used together, are a powerful way to reveal both strengths and weaknesses. We provide examples of data products, such as heatmaps, performance maps, detection timelines, and rank-based probability-of-detection curves.

Citation Download Citation

Gary Rubin and David H. Berger "A results-based process for evaluation of diverse visual analytics tools", Proc. SPIE 8740, Motion Imagery Technologies, Best Practices, and Workflows for Intelligence, Surveillance, and Reconnaissance (ISR), and Situational Awareness, 87400B (16 May 2013); https://doi.org/10.1117/12.2015396

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KEYWORDS

Video

Target detection

Video surveillance

Visual analytics

Cameras

Visualization

Algorithm development

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