Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Ordinal Class Imbalance with Ranking Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Recognizing Activities of Daily Living from Egocentric Images

Authors : Alejandro Cartas, Juan Marín, Petia Radeva, Mariella Dimiccoli

Published in: Pattern Recognition and Image Analysis

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Recognizing Activities of Daily Living (ADLs) has a large number of health applications, such as characterize lifestyle for habit improvement, nursing and rehabilitation services. Wearable cameras can daily gather large amounts of image data that provide rich visual information about ADLs than using other wearable sensors. In this paper, we explore the classification of ADLs from images captured by low temporal resolution wearable camera (2 fpm) by using a Convolutional Neural Networks (CNN) approach. We show that the classification accuracy of a CNN largely improves when its output is combined, through a random decision forest, with contextual information from a fully connected layer. The proposed method was tested on a subset of the NTCIR-12 egocentric dataset, consisting of 18,674 images and achieved an overall accuracy of 86% activity recognition on 21 classes.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Simple and Effective Multi-word Query Spotting in Handwritten Text Images
next chapter Strategies of Dictionary Usages for Sparse Representations for Pedestrian Classification
Literature
1.
Castro, D., Hickson, S., Bettadapura, V., Thomaz, E., Abowd, G., Christensen, H., Essa, I.: Predicting daily activities from egocentric images using deep learning. In: Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 75–82. ACM (2015)
2.
Fathi, A., Farhadi, A., Rehg, J.M.: Understanding egocentric activities. In: 2011 International Conference on Computer Vision, pp. 407–414. IEEE (2011)
3.
Fathi, A., Li, Y., Rehg, J.M.: Learning to recognize daily actions using gaze. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 314–327. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-33718-5_​23 CrossRef
4.
Gurrin, C., Joho, H., Hopfgartner, F., Zhou, L., Albatal, R.: Overview of NTCIR-12 lifelog task. In: NTCIR-12, National Institute of Informatics (NII)
5.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding (2014). arXiv preprint arXiv:​1408.​5093
6.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates Inc. (2012)
7.
Ma, M., Fan, H., Kitani, K.M.: Going deeper into first-person activity recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
8.
Martin-Lesende, I., Vrotsou, K., Vergara, I., Bueno, A., Diez, A., et al.: Design and validation of the vida questionnaire, for assessing instrumental activities of daily living in elderly people. J Gerontol. Geriat. Res. 4(214), 2 (2015)
9.
Nguyen, T.-H.-C., Nebel, J.-C., Florez-Revuelta, F.: Recognition of activities of daily living with egocentric vision: a review. Sensors (Basel), 16(1), 72 (2016). sensors-16-00072[PII]
10.
Pirsiavash, H., Ramanan, D.: Detecting activities of daily living in first-person camera views. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2847–2854. IEEE (2012)
11.
Schüssler-Fiorenza Rose, S.M., Stineman, M.G., Pan, Q., Bogner, H., Kurichi, J.E., Streim, J.E.: Potentially avoidable hospitalizations among people at different activity of daily living limitation stages. Health Serv. Res. 52(1), 132–155 (2016)CrossRef
12.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition (CVPR) (2015)
Metadata
Title
Recognizing Activities of Daily Living from Egocentric Images
Authors
Alejandro Cartas
Juan Marín
Petia Radeva
Mariella Dimiccoli
Copyright Year
2017
Book
Pattern Recognition and Image Analysis

Print ISBN: 978-3-319-58837-7

Electronic ISBN: 978-3-319-58838-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-58838-4_10

Premium Partner

    Image Credits