Authors:
Zenonas Theodosiou
1
;
Harris Partaourides
1
;
Tolga Atun
1
;
Simoni Panayi
1
and
Andreas Lanitis
2
;
1
Affiliations:
1
Research Centre on Interactive Media Smart Systems and Emerging Technologies, Nicosia, Cyprus
;
2
Department of Multimedia and Graphic Arts, Cyprus University of Technology, Limassol, Cyprus
Keyword(s):
Pedestrians, Safety, Visual Lifelogging, Egocentric Vision, First-person View, Dataset.
Abstract:
Egocentric vision, which relates to the continuous interpretation of images captured by wearable cameras, is increasingly being utilized in several applications to enhance the quality of citizens life, especially for those with visual or motion impairments. The development of sophisticated egocentric computer vision techniques requires automatic analysis of large databases of first-person point of view visual data collected through wearable devices. In this paper, we present our initial findings regarding the use of wearable cameras for enhancing the pedestrians safety while walking in city sidewalks. For this purpose, we create a first-person database that entails annotations on common barriers that may put pedestrians in danger. Furthermore, we derive a framework for collecting visual lifelogging data and define 24 different categories of sidewalk barriers. Our dataset consists of 1796 annotated images covering 1969 instances of barriers. The analysis of the dataset by means of obj
ect classification algorithms, depict encouraging results for further study.
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