Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61898
Title: Hazard Control in Industrial Environments: A Knowledge-Vision-Based Approach
Contributor(s): de Oliveira, Caterine Silva (author); Sanin, Cesar  (author)orcid ; Szczerbicki, Edward (author)
Publication Date: 2017-09-01
DOI: 10.1007/978-3-319-67223-6_23
Handle Link: https://hdl.handle.net/1959.11/61898
Abstract: 

This paper proposes the integration of image processing techniques (such as image segmentation, feature extraction and selection) and a knowledge representation approach in a framework for the development of an automatic system able to identify, in real time, unsafe activities in industrial environments. In this framework, the visual information (feature extraction) acquired from video-camera images and other context based gathered data are represented as Set of Experience Knowledge Structure (SOEKS), a formal decision event for reasoning and risk evaluation. Then, grouped sets of decisions from the same category are stored as decisional experience Decisional DNA (DDNA) to support future decision making events in similar input images. Unlike the existing sensor and vision-based approaches, that required rewriting most of the code when a condition, situation or requirement changes, our platform is an adaptable system capable of working in a variety of video analysis scenarios. Depending on the safety requirements of each industrial environment, users can feed the system with flexible rules and in the end, the platform provides decision makers with hazard evaluations that reuse experience for event identification and correction.

Publication Type: Conference Publication
Conference Details: ISAT 2017: 38th International Conference on Information Systems Architecture and Technology, Szklarska Poręba, Poland, 17th- 19th September, 2017
Source of Publication: Information Systems Architecture and Technology, p. 243-252
Publisher: Springer
Place of Publication: Germany
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Series Name: Advances in Intelligent Systems and Computing
Series Number : 657
Appears in Collections:Conference Publication
School of Science and Technology

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