PET: An eye-tracking dataset for animal-centric Pascal object classes

Syed Omer Gilani; Ramanathan Subramanian; Yan Yan; David Melcher; Nicu Sebe; Stefan Winkler

doi:10.1109/ICME.2015.7177450

PET: An eye-tracking dataset for animal-centric Pascal object classes

Syed Omer Gilani, Ramanathan Subramanian, Yan Yan, David Melcher, Nicu Sebe, Stefan Winkler

Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present PET- the Pascal animal classes Eye Tracking database. Our database comprises eye movement recordings compiled from forty users for the bird, cat, cow, dog, horse and sheep trainval sets from the VOC 2012 image set. Different from recent eye-tracking databases such as [1, 2], a salient aspect of PET is that it contains eye movements recorded for both the free-viewing and visual search task conditions. While some differences in terms of overall gaze behavior and scanning patterns are observed between the two conditions, a very similar number of fixations are observed on target objects for both conditions. As a utility application, we show how feature pooling around fixated locations enables enhanced (animal) object classification accuracy.

Original language	English (US)
Title of host publication	2015 IEEE International Conference on Multimedia and Expo, ICME 2015
Publisher	IEEE Computer Society
ISBN (Electronic)	9781479970827
DOIs	https://doi.org/10.1109/ICME.2015.7177450
State	Published - Aug 4 2015
Event	IEEE International Conference on Multimedia and Expo, ICME 2015 - Turin, Italy Duration: Jun 29 2015 → Jul 3 2015

Publication series

Name	Proceedings - IEEE International Conference on Multimedia and Expo
Volume	2015-August
ISSN (Print)	1945-7871
ISSN (Electronic)	1945-788X

Conference

Conference	IEEE International Conference on Multimedia and Expo, ICME 2015
Country/Territory	Italy
City	Turin
Period	6/29/15 → 7/3/15

Keywords

Animal-centric object classes
Eye movements
Free-viewing vs. Visual search
PET
Pascal VOC

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications

Access to Document

10.1109/ICME.2015.7177450

Cite this

Gilani, S. O., Subramanian, R., Yan, Y., Melcher, D., Sebe, N., & Winkler, S. (2015). PET: An eye-tracking dataset for animal-centric Pascal object classes. In 2015 IEEE International Conference on Multimedia and Expo, ICME 2015 [7177450] (Proceedings - IEEE International Conference on Multimedia and Expo; Vol. 2015-August). IEEE Computer Society. https://doi.org/10.1109/ICME.2015.7177450

PET : An eye-tracking dataset for animal-centric Pascal object classes. / Gilani, Syed Omer; Subramanian, Ramanathan; Yan, Yan et al.

2015 IEEE International Conference on Multimedia and Expo, ICME 2015. IEEE Computer Society, 2015. 7177450 (Proceedings - IEEE International Conference on Multimedia and Expo; Vol. 2015-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gilani, SO, Subramanian, R, Yan, Y, Melcher, D, Sebe, N & Winkler, S 2015, PET: An eye-tracking dataset for animal-centric Pascal object classes. in 2015 IEEE International Conference on Multimedia and Expo, ICME 2015., 7177450, Proceedings - IEEE International Conference on Multimedia and Expo, vol. 2015-August, IEEE Computer Society, IEEE International Conference on Multimedia and Expo, ICME 2015, Turin, Italy, 6/29/15. https://doi.org/10.1109/ICME.2015.7177450

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abstract = "We present PET- the Pascal animal classes Eye Tracking database. Our database comprises eye movement recordings compiled from forty users for the bird, cat, cow, dog, horse and sheep trainval sets from the VOC 2012 image set. Different from recent eye-tracking databases such as [1, 2], a salient aspect of PET is that it contains eye movements recorded for both the free-viewing and visual search task conditions. While some differences in terms of overall gaze behavior and scanning patterns are observed between the two conditions, a very similar number of fixations are observed on target objects for both conditions. As a utility application, we show how feature pooling around fixated locations enables enhanced (animal) object classification accuracy.",

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PET: An eye-tracking dataset for animal-centric Pascal object classes

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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