Paper Info
Title
Understanding the Nature of First-Person Videos: Characterization and Classification Using Low-Level Features
Abstract
First-person view (FPV) video data is set to proliferate rapidly, due to many consumer wearable-camera devices coming onto the market. Research into FPV (or \"egocentric\") vision is also becoming more common in the computer vision community. However, it is still unclear what the fundamental characteristics of such data are. How is it really different from third-person view (TPV) data? Can all FPV data be treated the same? In this first attempt to approach these questions in a quantitative and empirical manner, we analyzed a meta-collection of 21 FPV and TPV datasets totaling more than 165 hours of video. We performed the first quantitative characterization of FPV videos over multiple datasets, encompassing virtually all available FPV datasets. Validating this characterization, linear classifiers trained on low-level features to perform FPV-versus-TPV classification achieved good baseline performance. Accuracy peaked at 81% for 2-minute clips, but 67% accuracy was achieved even with 1-second clips. Our low-level features are fast to compute and do not require annotation. Overall, our work uncovered insights regarding the basic nature and characteristics of FPV data.
Year
DOI
Venue
2014
10.1109/CVPRW.2014.85
Computer Vision and Pattern Recognition Workshops
Keywords
Field
DocType
image classification,video signal processing,FPV-versus-TPV classification,first-person view video data,linear classifiers,low-level features,third-person view data
Computer vision,Annotation,Computer science,Artificial intelligence,Machine learning,CLIPS
Conference
ISSN
Citations 
PageRank 
2160-7508
7
0.46
References 
Authors
19
4
Name
Order
Citations
PageRank
Cheston Tan115515.27
Goh, H.270.46
Vijay Chandrasekhar319122.83
Liyuan Li491261.31