Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams

Martín Menchón*, Estefanía Talavera, José Massa, Petia Radeva

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
16 Downloads (Pure)


The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 Workshops
Subtitle of host publicationGlasgow, UK, August 23–28, 2020, Proceedings
EditorsAdrien Bartoli, Andrea Fusiello
Place of PublicationCham
Number of pages16
ISBN (Electronic)978-3-030-66823-5
ISBN (Print)978-3-030-66822-8
Publication statusPublished - 2020
Externally publishedYes
EventWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom


  • Behaviour analysis
  • Data mining
  • Egocentric vision
  • Lifelogging
  • Pattern discovery


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