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Review of Trends in Automatic Human Activity Recognition in Vehicle Based in Synthetic Data

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12490))

Abstract

Driverless vehicles are more and more becoming a reality. However, people still have some concerns in using them, the main concern is fear, hence the importance of creating a surveillance system inside those vehicles. For the detection and classification of human movements to be possible it is necessary to train the system with data representative enough for all kinds of possibilities. Although the production of large quantities of data becomes an expensive process and adds the problem of data protection, the use of synthetic data once they are artificially generated allows lower costs and eliminates the problem of data protection. A bibliographic study was carried out in this paper with articles from 2017 or later on the use of synthetic data. In these studies, it is noted that synthetic data is widely used with good results. As far as image capture is concerned, they show that 3D cameras have better results, but they are more expensive, so 2D cameras are more often used with later conversion to 3D images. The stitched puppet (SP) model is capable of adapting to the most difficult poses having obtained good results in its application in the FAUST dataset.

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Acknowledgments

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. Human and material resources have also been supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project number 039334; Funding Reference: POCI-01-0247-FEDER-039334].

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Authors and Affiliations

  1. Centre Algoritmi, University of Minho, 4710-057, Braga, Portugal

    Ana Coimbra, Cristiana Neto, Diana Ferreira, Júlio Duarte, Daniela Oliveira, Francini Hak, Nicolas Lori, António Abelha & José Machado

  2. Bosch Car Multimedia, 4705-820, Braga, Portugal

    Ana Coimbra, Cristiana Neto, Diana Ferreira, Júlio Duarte, Daniela Oliveira, Francini Hak, Filipe Gonçalves, Joaquim Fonseca, Nicolas Lori, António Abelha & José Machado

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  1. Ana Coimbra
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Correspondence to Nicolas Lori .

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Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Coimbra, A. et al. (2020). Review of Trends in Automatic Human Activity Recognition in Vehicle Based in Synthetic Data. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_35

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  • DOI: https://doi.org/10.1007/978-3-030-62365-4_35

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