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24 April 2024 |
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Article overview
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Identifying First-person Camera Wearers in Third-person Videos | | Chenyou Fan
; Jangwon Lee
; Mingze Xu
; Krishna Kumar Singh
; Yong Jae Lee
; David J. Crandall
; Michael S. Ryoo
; | | Date: |
20 Apr 2017 | | Abstract: | We consider scenarios in which we wish to perform joint scene understanding,
object tracking, activity recognition, and other tasks in environments in which
multiple people are wearing body-worn cameras while a third-person static
camera also captures the scene. To do this, we need to establish person-level
correspondences across first- and third-person videos, which is challenging
because the camera wearer is not visible from his/her own egocentric video,
preventing the use of direct feature matching. In this paper, we propose a new
semi-Siamese Convolutional Neural Network architecture to address this novel
challenge. We formulate the problem as learning a joint embedding space for
first- and third-person videos that considers both spatial- and motion-domain
cues. A new triplet loss function is designed to minimize the distance between
correct first- and third-person matches while maximizing the distance between
incorrect ones. This end-to-end approach performs significantly better than
several baselines, in part by learning the first- and third-person features
optimized for matching jointly with the distance measure itself. | | Source: | arXiv, 1704.6340 | | Services: | Forum | Review | PDF | Favorites | |
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