Evolving classification of agents’ behaviors: a general approach

Iglesias, José Antonio; Angelov, Plamen; Ledezma, Agapito; Sanchís, Araceli

doi:10.1007/s12530-010-9008-8

Cited by 60 publications

(23 citation statements)

References 26 publications

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“…Some methods based on neuro-fuzzy systems (NFS) have also been proposed for human gesture and action recognition [110,111]. In addition to this, evolving systems [112,113] are also very successful in behaviour recognition. Traditional vision-based human action recognition approaches employ the spatial or temporal features followed by a generic classifier for action representation and classification.…”

Section: Fuzzy Logic-based Approachesmentioning

confidence: 99%

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

2017

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Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

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Section: Fuzzy Logic-based Approachesmentioning

confidence: 99%

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

2017

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“…This way, it is not necessary to store all the calls data-what is essential in this research-since a recursive algorithm that gives exact (not approximate) expression for the density is used. This technique has been used for anomaly detection in different environments, such as video streams [43], recognition of human activities in an intelligent home environment [44], and many others.…”

Section: Recursive Density Estimation (Rde)mentioning

confidence: 99%

Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering

et al. 2017

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Abstract:In the competitive telecommunications market, the information that the mobile telecom operators can obtain by regularly analysing their massive stored call logs, is of great interest. Although the data that can be extracted nowadays from mobile phones have been enriched with much information, the data solely from the call logs can give us vital information about the customers. This information is usually related with the calling behaviour of their customers and it can be used to manage them. However, the analysis of these data is normally very complex because of the vast data stream to analyse. Thus, efficient data mining techniques need to be used for this purpose. In this paper, a novel approach to analyse call detail records (CDR) is proposed, with the main goal to extract and cluster different calling patterns or behaviours, and to detect outliers. The main novelty of this approach is that it works in real-time using an evolving and recursive framework.

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“…Thus, the idea proposed in [13] is the same that is proposed in this paper; however, the method for obtaining the user models and how they are keep up to date (although both methods are based on evolving systems), is different. The method proposed in [13] is extended to other different domains in [14].…”

Section: Background and Related Workmentioning

confidence: 99%