UT Egocentric (UT Ego) Dataset [Download (1.4GB)]
Information
Evaluation
Publications
People
The Univ.
of Texas at Austin Egocentric (UT Ego) Dataset
contains 4 videos captured from head-mounted
cameras. Each video is about 3-5 hours long,
captured in a natural, uncontrolled setting.
We used the Looxcie wearable camera, which captures video at 15 fps at 320 x 480 resolution. Four subjects wore the camera for us: one undergraduate student, two graduate students, and one office worker. The videos capture a variety of activities such as eating, shopping, attending a lecture, driving, and cooking.
* Due to privacy reasons, we are able to share only 4 of the 10 videos originally captured (one from each subject). They correspond to the test videos that we evaluate on in both the CVPR 2012 and CVPR 2013 papers.
DataWe used the Looxcie wearable camera, which captures video at 15 fps at 320 x 480 resolution. Four subjects wore the camera for us: one undergraduate student, two graduate students, and one office worker. The videos capture a variety of activities such as eating, shopping, attending a lecture, driving, and cooking.
* Due to privacy reasons, we are able to share only 4 of the 10 videos originally captured (one from each subject). They correspond to the test videos that we evaluate on in both the CVPR 2012 and CVPR 2013 papers.
- The UT Ego Dataset can be downloaded here. It is 1.4 GB. Note that the human faces in the videos are artificially blurred due to privacy reasons.
- Ground-truth
annotations for important regions can be
downloaded here.
Evaluation
- Important region prediction
Can be evaluated with provided
ground-truth above. Training/testing should be conducted in a
leave-one-out fashion (i.e., train on 3 videos
test on 1
remaining video). A region whose overlap
score (intersection over union) with any
ground-truth region is greater than 0.5 should be considered as a true
positive (i.e., important object). See "Important
region prediction
accuracy"
in
Sec. 4 of CVPR
2012 for
guidance on prior studies.
- Summarization
Requires human subject
studies for evaluation. See "User studies to
evaluate summaries" in
Sec. 4 of CVPR
2012 and "Evaluating summary quality" in
Sec. 4 of CVPR
2013 for guidance on prior studies.
Publications
 |
Y. J. Lee, J. Ghosh, and K. Grauman. Discovering Important People and Objects for Egocentric Video Summarization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. [project page] |
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Z. Lu and K. Grauman. Story-Driven Summarization for Egocentric Video. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013. [project page] |
People
