Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams

Martín Menchón; Estefanía Talavera; José Massa; Petia Radeva

doi:10.1007/978-3-030-66823-5_28

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 proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

12 Downloads (Pure)

Abstract

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 language	English
Title of host publication	Computer Vision – ECCV 2020 Workshops
Subtitle of host publication	Glasgow, UK, August 23–28, 2020, Proceedings
Editors	Adrien Bartoli, Andrea Fusiello
Place of Publication	Cham
Publisher	Springer
Pages	469-484
Number of pages	16
ISBN (Electronic)	978-3-030-66823-5
ISBN (Print)	978-3-030-66822-8
DOIs	https://doi.org/10.1007/978-3-030-66823-5_28
Publication status	Published - 2020
Externally published	Yes
Event	16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020 Conference number: 16 https://eccv2020.eu/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer,
Volume	12538=
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th European Conference on Computer Vision, ECCV 2020
Abbreviated title	ECCV
Country/Territory	United Kingdom
City	Glasgow
Period	23/08/20 → 28/08/20
Internet address	https://eccv2020.eu/

Keywords

Behaviour analysis
Data mining
Egocentric vision
Lifelogging
Pattern discovery

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10.1007/978-3-030-66823-5_28

Menchón2020behaviouralAccepted author version, 5.69 MB

Cite this

Menchón, M., Talavera, E., Massa, J., & Radeva, P. (2020). Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams. In A. Bartoli, & A. Fusiello (Eds.), Computer Vision – ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings (pp. 469-484). (Lecture Notes in Computer Science; Vol. 12538=). Springer. https://doi.org/10.1007/978-3-030-66823-5_28

@inproceedings{13ae3a5f93df4a469c1f4a712189f19d,

title = "Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams",

abstract = "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{\textquoteright}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.",

keywords = "Behaviour analysis, Data mining, Egocentric vision, Lifelogging, Pattern discovery",

author = "Mart{\'i}n Mench{\'o}n and Estefan{\'i}a Talavera and Jos{\'e} Massa and Petia Radeva",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020, ECCV ; Conference date: 23-08-2020 Through 28-08-2020",

year = "2020",

doi = "10.1007/978-3-030-66823-5_28",

language = "English",

isbn = "978-3-030-66822-8",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

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editor = "Adrien Bartoli and Andrea Fusiello",

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}

Menchón, M, Talavera, E, Massa, J & Radeva, P 2020, Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams. in A Bartoli & A Fusiello (eds), Computer Vision – ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings. Lecture Notes in Computer Science, vol. 12538=, Springer, Cham, pp. 469-484, 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 23/08/20. https://doi.org/10.1007/978-3-030-66823-5_28

Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams. / Menchón, Martín; Talavera, Estefanía; Massa, José et al.
Computer Vision – ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings. ed. / Adrien Bartoli; Andrea Fusiello. Cham: Springer, 2020. p. 469-484 (Lecture Notes in Computer Science; Vol. 12538=).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams

AU - Menchón, Martín

AU - Talavera, Estefanía

AU - Massa, José

AU - Radeva, Petia

N1 - Conference code: 16

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Behaviour analysis

KW - Data mining

KW - Egocentric vision

KW - Lifelogging

KW - Pattern discovery

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U2 - 10.1007/978-3-030-66823-5_28

DO - 10.1007/978-3-030-66823-5_28

M3 - Conference contribution

SN - 978-3-030-66822-8

T3 - Lecture Notes in Computer Science

SP - 469

EP - 484

BT - Computer Vision – ECCV 2020 Workshops

A2 - Bartoli, Adrien

A2 - Fusiello, Andrea

PB - Springer

CY - Cham

T2 - 16th European Conference on Computer Vision, ECCV 2020

Y2 - 23 August 2020 through 28 August 2020

ER -

Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams

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Keywords

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