On the use of semantic technologies for video analytics

Greco, Luca; Ritrovato, Pierluigi; Vento, Mario

doi:10.1007/s12652-020-02021-y

Cited by 8 publications

(4 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Large-scale image retrieval systems necessarily have high demands on the time overhead of retrieval [ 9 ]. The performance problem is a difficult problem that must be solved for retrieval on a collection of images of hundreds of millions of sizes [ 10 ]. The basis of image retrieval lies in the similarity calculation between the query image and the database image in the feature space.…”

Section: Related Workmentioning

confidence: 99%

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

Ruan

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In this paper, a high-level semantic recognition model is used to parse the video content of human sports under engineering management, and the stream shape of the previous layer is embedded in the convolutional operation of the next layer, so that each layer of the convolutional neural network can effectively maintain the stream structure of the previous layer, thus obtaining a video image feature representation that can reflect the image nearest neighbor relationship and association features. The method is applied to image classification, and the experimental results show that the method can extract image features more effectively, thus improving the accuracy of feature classification. Since fine-grained actions usually share a very high similarity in phenotypes and motion patterns, with only minor differences in local regions, inspired by the human visual system, this paper proposes integrating visual attention mechanisms into the fine-grained action feature extraction process to extract features for cues. Taking the problem as the guide, we formulate the athlete’s tacit knowledge management strategy and select the distinctive freestyle aerial skills national team as the object of empirical analysis, compose a more scientific and organization-specific tacit knowledge management program, exert influence on the members in the implementation, and revise to form a tacit knowledge management implementation program with certain promotion value. Group behavior can be identified by analyzing the behavior of individuals and the interaction information between individuals. Individual interactions in a group can be represented by individual representations, and the relationship between individual behaviors can be analyzed by modeling the relationship between individual representations. The performance improvement of the method on mismatched datasets is comparable between the long-short time network based on temporal information and the language recognition method with high-level semantic embedding vectors, with the two methods improving about 12.6% and 23.0%, respectively, compared with the method using the original model and with the i-vector baseline system based on the support vector machine classification method with radial basis functions, with performance improvements about 10.10% and 10.88%, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

Ruan

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…An application of ontology update to cloud forensics can be observed in Kebande et al, 25 wherein potential digital evidence (PDE) information was mapped with ontology data in order to provide effective retrieval of data. Another application of ontology‐based data retrieval applied to video data can be observed from Greco et al, 26 wherein a Knowledge‐Based Computer Vision model is deployed. This work is extended in Salguero et al 27 with the help of class expression learning for automatic recognition of daily living activities from semantic data.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel semantic representation approach for web documents using deep entity linking and multidocument support

Urkude

Pandey

2022

Int J Communication

View full text Add to dashboard Cite

Summary Representing semantic web information in the form of existing formats like extensible mark‐up language (XML), resource description framework (RDF) or web ontology language (OWL) is limited by the inability to maintain intradocument relationships between different semantic entities. Moreover, these standards are built for generic data representation and thus do not have inherent support for the semantic description of the document and, consequently, do not support features like multidocument linkages and context‐sensitivity. In order to remove these drawbacks, a semantic web‐specific document representation approach, namely, deep semantic XML (DS‐XML) using deep linking and machine learning, is presented in this paper. The DS‐XML approach facilitates the use of semantics with the help of a novel document structure that focuses on defining any web document in a semantic form. It includes ontologies and linkage between different vocabularies, which assists in achieving semantic interoperability. In order to make this approach future proof, a novel deep linking approach is integrated at the document level, which allows DS‐XML to understand more about the contents of the document and represent information in an aggregated manner. The deep linking layer is combined with the machine learning‐based classification layer that allows the document information to be represented in an interactive manner. The proposed DS‐XML approach is tested on different document retrieval applications, including a few Internet of Things (IoT) applications, and the representation performance is found to be superior to the existing semantic approach (XML, RDF and OWL).

show abstract

“…• An ontology is used to represent, name and define the categories and properties of, and the relationships between, objects, data and entities (Greco, Ritrovato, & Vento, 2020;Rosen, von Wichert, Lo, & Bettenhausen, 2015). • A digital twin is used as a model of a present or future entity of the real world, which describes its properties on the basis of real data and simulates its behaviour on the basis of algorithms and simulations, thus closing the gap between the real and virtual world (Kritzinger, Karner, Traar, Henjes, & Sihn, 2018;Qi & Tao, 2018).…”

Section: Data Modelingmentioning

confidence: 99%

Characterizing the Capabilities of Internet of Things Analytics Through Taxonomy and Reference Architecture

Daradkeh

2022

Journal of Information Technology Research

View full text Add to dashboard Cite

The increasing prevalence of business cases utilizing Internet of Things (IoT) analytics, coupled with the diversity of IoT analytics platforms and their capabilities, poses an immense challenge for organizations seeking to make the best choice of IoT analytics platform for their specific use cases. Aiming to characterize the capabilities of IoT analytics, this article presents a reference architecture for IoT analytics platforms created through a qualitative content analysis of online reviews and published implementation architectures of IoT analytics platforms. A further contribution is a taxonomy of the functional and cross-functional capabilities of IoT analytics platforms derived from the analysis of published use cases and related business surveys. Both the reference architecture and the associated taxonomy provide a theoretical basis for further research into IoT analytics capabilities and should therefore facilitate the evaluation, selection and adoption of IoT analytics solutions through a unified description of their capabilities and functional requirements.

show abstract

On the use of semantic technologies for video analytics

Cited by 8 publications

References 93 publications

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

A novel semantic representation approach for web documents using deep entity linking and multidocument support

Characterizing the Capabilities of Internet of Things Analytics Through Taxonomy and Reference Architecture

Contact Info

Product

Resources

About