Abstract

Thanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones and glasses, we have access to a plethora of videos captured from the first person (egocentric) perspective. Capturing the world from the perspective of one's self, egocentric videos bear characteristics distinct from the more traditional third-person (exocentric) videos. In many computer vision tasks (e.g. identification, action recognition, face recognition, pose estimation, etc.), the human actors are the main focus. Hence, detecting, localizing, and recognizing the human actor is often incorporated as a vital component. In an egocentric video however, the person behind the camera is often the person of interest. This would change the nature of the task at hand, given that the camera holder is usually not visible in the content of his/her egocentric video. In other words, our knowledge about the visual appearance, pose, etc. on the egocentric camera holder is very limited, suggesting reliance on other cues in first person videos. First and third person videos have been separately studied in the past in the computer vision community. However, the relationship between first and third person vision has yet to be fully explored. Relating these two views systematically could potentially benefit many computer vision tasks and applications. This thesis studies this relationship in several aspects. We explore supervised and unsupervised approaches for relating these two views seeking different objectives such as identification, temporal alignment, and action classification. We believe that this exploration could lead to a better understanding the relationship of these two drastically different sources of information.

Notes

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Graduation Date

2018

Semester

Summer

Advisor

Borji, Ali

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0007161

URL

http://purl.fcla.edu/fcla/etd/CFE0007151

Language

English

Release Date

August 2018

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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