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Computers, Volume 2, Issue 2 (June 2013), Pages 67-131

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Research

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Open AccessArticle A Characterization of the Utility of Using Artificial Intelligence to Test Two Artificial Intelligence Systems
Computers 2013, 2(2), 67-87; doi:10.3390/computers2020067
Received: 28 December 2012 / Revised: 4 February 2013 / Accepted: 22 May 2013 / Published: 31 May 2013
Cited by 4 | PDF Full-text (729 KB) | HTML Full-text | XML Full-text
Abstract
An artificial intelligence system, designed for operations in a real-world environment faces a nearly infinite set of possible performance scenarios. Designers and developers, thus, face the challenge of validating proper performance across both foreseen and unforeseen conditions, particularly when the artificial intelligence is
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An artificial intelligence system, designed for operations in a real-world environment faces a nearly infinite set of possible performance scenarios. Designers and developers, thus, face the challenge of validating proper performance across both foreseen and unforeseen conditions, particularly when the artificial intelligence is controlling a robot that will be operating in close proximity, or may represent a danger, to humans. While the manual creation of test cases allows limited testing (perhaps ensuring that a set of foreseeable conditions trigger an appropriate response), this may be insufficient to fully characterize and validate safe system performance. An approach to validating the performance of an artificial intelligence system using a simple artificial intelligence test case producer (AITCP) is presented. The AITCP allows the creation and simulation of prospective operating scenarios at a rate far exceeding that possible by human testers. Four scenarios for testing an autonomous navigation control system are presented: single actor in two-dimensional space, multiple actors in two-dimensional space, single actor in three-dimensional space, and multiple actors in three-dimensional space. The utility of using the AITCP is compared to that of human testers in each of these scenarios. Full article

Review

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Open AccessReview A Review on Video-Based Human Activity Recognition
Computers 2013, 2(2), 88-131; doi:10.3390/computers2020088
Received: 29 November 2012 / Revised: 21 February 2013 / Accepted: 30 April 2013 / Published: 5 June 2013
Cited by 44 | PDF Full-text (1295 KB) | HTML Full-text | XML Full-text
Abstract
This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing
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This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems. Full article
(This article belongs to the Special Issue Activity Detection and Novel Sensing Technologies)

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