Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis

Conigliaro, Davide; Ferrario, Roberta; Hudelot, Céline; Porello, Daniele

doi:10.1016/b978-0-12-809276-7.00016-3

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…DOLCE is here extended to include the category of Concepts (C) and Roles (RL) and the relation of classification (CF), as we shall see below; their formalization is taken from [23]. 8…”

Section: The Formalization Of Dolce In First-order Logicmentioning

confidence: 99%

“…These modules facilitate the application and coherent use of the ontology. Some extensions tackle knowledge representation's specific issues, like the modeling of roles [23], artifacts [30,3], and modules [10], others attempts an integration with machine learning and in particular computer vision [8]. Extensions to the modeling of social [6,28,26] and cognitive aspects [9,1] have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Some extensions tackle knowledge representation's specific issues, like the modeling of roles by Masolo et al (2004), of artifacts by Vieu et al (2008) and by Borgo et al (2014), and of modules by Ferrario and Porello (2015). Others showed a possible integration with machine learning and in particular computer vision (Conigliaro et al, 2017). Extensions to the modeling of social (Bottazzi and Ferrario, 2009;Porello et al, 2013Porello et al, , 2014a and cognitive aspects (Ferrario and Oltramari, 2004;Biccheri et al, 2020) have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

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DOLCE: A descriptive ontology for linguistic and cognitive engineering1

Borgo

Ferrario

Gangemi

et al. 2022

Self Cite

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dolce, the first top-level (foundational) ontology to be axiomatized, has remained stable for twenty years and today is broadly used in a variety of domains. dolce is inspired by cognitive and linguistic considerations and aims to model a commonsense view of reality, like the one human beings exploit in everyday life in areas as diverse as socio-technical systems, manufacturing, financial transactions and cultural heritage. dolce clearly lists the ontological choices it is based upon, relies on philosophical principles, is richly formalized, and is built according to well-established ontological methodologies, e.g. OntoClean. Because of these features, it has inspired most of the existing top-level ontologies and has been used to develop or improve standards and public domain resources (e.g. CIDOC CRM, DBpedia and WordNet). Being a foundational ontology, dolce is not directly concerned with domain knowledge. Its purpose is to provide the general categories and relations needed to give a coherent view of reality, to integrate domain knowledge, and to mediate across domains. In these 20 years dolce has shown that applied ontologies can be stable and that interoperability across reference and domain ontologies is a reality. This paper briefly introduces the ontology and shows how to use it on a few modeling cases.

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“…DOLCE is here extended to include the category of Concepts (C) and Roles (RL) and the relation of classification (CF), as we shall see below; their formalization is taken from [23]. 8…”

Section: The Formalization Of Dolce In First-order Logicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DOLCE: A descriptive ontology for linguistic and cognitive engineering1

Borgo

Ferrario

Gangemi

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…us, consideration must be given to the limitations and domain knowledge of the computer vision system that extracts the visual data captured by the camera [47,48]. at is, an image recognition application process should be developed based on an analysis of South Korean laws and standards.…”

Section: Computer Visionmentioning

confidence: 99%

Computer Vision Process Development regarding Worker’s Safety Harness and Hook to Prevent Fall Accidents: Focused on System Scaffolds in South Korea

Lim

Jung

Park

et al. 2022

Advances in Civil Engineering

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In South Korea, industrial accidents continue to increase in frequency, with construction accidents accounting for more than a third of all industrial accidents. Specifically, by preventing fall accidents, the death rate from accidents can be reduced by 50%. Fall protection is required to prevent fall accidents, and investigating the reinforcement of the worker’s safety harness and hook fastening becomes imperative. This requires automation of computer vision confirmation of the safety harness and hook fastening. As the accident risk can be reduced by an effective safety culture in the system, it is necessary to monitor safety on site through a construction safety automation system. Therefore, the objective of this study is to develop a computer vision process for safety harness and hook for preventing fall accidents in South Korea’s construction industry. This study focuses on system scaffolds that are widely used at construction sites. The application of this methodology to sample sites and field cases established its applicability. The proposed computer vision application methodology will serve as the foundation for visualization research in the construction industry, and image recognition will help reduce the safety accident rate. Accidents caused by failure to use a safety harness and hook can be reduced in South Korea as well as globally. Additionally, this methodology is applicable to roof construction, tower crane installation and dismantling work, as well as steel tower installation and dismantling.

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“…Research works like [4] are dedicated to image interpretations while using ontologies. A DL process (e.g.…”

Section: Explainablity Semantics and Ontologiesmentioning

confidence: 99%

Towards Ontologically Explainable Classifiers

Bourguin

Lewandowski

Bouneffa

et al. 2021

Lecture Notes in Computer Science

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In order to meet the explainability requirement of AI using Deep Learning (DL), this paper explores the contributions and feasibility of a process designed to create ontologically explainable classifiers while using domain ontologies. The approach is illustrated with the help of the Pizzas ontology that is used to create a synthetic image classifier that is able to provide visual explanations concerning a selection of ontological features. The approach is implemented by completing a DL model with ontological tensors that are generated from the ontology expressed in Description Logic.

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Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis

Cited by 6 publications

References 27 publications

DOLCE: A descriptive ontology for linguistic and cognitive engineering1

DOLCE: A descriptive ontology for linguistic and cognitive engineering1

Computer Vision Process Development regarding Worker’s Safety Harness and Hook to Prevent Fall Accidents: Focused on System Scaffolds in South Korea

Towards Ontologically Explainable Classifiers

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