Projects per year
Description
The proliferation of wearable cameras has accelerated and facilitated a surge in research on analysis of egocentric videos. This relatively new field has relatively few research datasets. A significant share of publicly available egocentric datasets is purposely acquired for activity recognition, video summarization, object detection, and behavioural understanding. Here we share our dataset that is focused on personal localization and mapping.
We collected our dataset on the university campus, documenting a user’s typical morning. The recording is always initiated at the building entrance, at which point the user enters and triggers the session. The dataset was collected indoors before working hours to ensure people were not mistakenly captured, avoiding potential privacy challenges. To further guard ourselves, we recorded the dataset exclusively in an indoor environment to limit strangers’ appearance in the field of view. The five sessions cover a one-month period. During recording, the user’s operation was in line with a loosely worded script to ensure that multiple visits were made to a range of locations. In total, we captured nine distinct stations: Entrance, 3d-lab, Kitchen 1, Kitchen 2, Cafe-area, Lab, Printer 1, Printer 2 and Office.
Data also available at https://cvip.computing.dundee.ac.uk/qmb-morning/ link, made available under Creative Commons Attribution 4.0 International (CC BY 4.0) Licence
We collected our dataset on the university campus, documenting a user’s typical morning. The recording is always initiated at the building entrance, at which point the user enters and triggers the session. The dataset was collected indoors before working hours to ensure people were not mistakenly captured, avoiding potential privacy challenges. To further guard ourselves, we recorded the dataset exclusively in an indoor environment to limit strangers’ appearance in the field of view. The five sessions cover a one-month period. During recording, the user’s operation was in line with a loosely worded script to ensure that multiple visits were made to a range of locations. In total, we captured nine distinct stations: Entrance, 3d-lab, Kitchen 1, Kitchen 2, Cafe-area, Lab, Printer 1, Printer 2 and Office.
Data also available at https://cvip.computing.dundee.ac.uk/qmb-morning/ link, made available under Creative Commons Attribution 4.0 International (CC BY 4.0) Licence
Date made available | 29 Jun 2021 |
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Publisher | University of Dundee |
Temporal coverage | 25 Jan 2020 - 15 Mar 2020 |
Date of data production | 25 Jan 2020 - 15 Mar 2020 |
Geospatial point | 56.45871134267333, -2.982786445488342Show on map |
Projects
- 1 Finished
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ACE-LP - Augmenting Communication Using Environmental Data to Drive Language Prediction (Joint with University of Cambridge)
Black, R. (Investigator), McKenna, S. (Investigator), Waller, A. (Investigator) & Zhang, J. (Investigator)
Engineering and Physical Sciences Research Council
1/02/16 → 31/01/20
Project: Research
Research output
- 1 Conference contribution
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Egomap: Hierarchical First-person Semantic Mapping
Suveges, T. & McKenna, S. (Lead / Corresponding author), 2021, Pattern Recognition. ICPR International Workshops and Challenges : Virtual Event, January 10–15, 2021, Proceedings, Part III. Del Bimbo, A., Cucchiara, R., Sclaroff, S., Farinella, G. M., Mei, T., Bertini, M., Escalante, H. J. & Vezzani, R. (eds.). Switzerland: Springer , p. 348-363 16 p. (Lecture Notes in Computer Science; vol. 12663).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile1 Citation (Scopus)123 Downloads (Pure)
Student theses
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Extracting contextual information from egocentric videos
Süveges, T. (Author), McKenna, S. (Supervisor) & Waller, A. (Supervisor), 2021Student thesis: Doctoral Thesis › Doctor of Philosophy
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