EgoRoutine: Topic modelling for routine discovery from egocentric photo-streams

doi: 10.4121/16577627.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/16577627
Datacite citation style:
Estefanía Talavera Martínez; Petia Radeva; Nicolai Petkov (2021): EgoRoutine: Topic modelling for routine discovery from egocentric photo-streams. Version 1. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed.

Published in:
Estefania Talavera, Carolin Wuerich, Nicolai Petkov, Petia Radeva, Topic modelling for routine discovery from egocentric photo-streams, Pattern Recognition, Volume 104, 2020, 107330, ISSN 0031-3203,
  • 2021-09-10 first online, published, posted
University of Groningen, Johann Bernoulli Institute
University of Barcelona, Department Mathematics and Computer Science and Computer Vision Center


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