Paper Info
Title
Identifying Same Persons from Temporally Synchronized Videos Taken by Multiple Wearable Cameras.
Abstract
ideo-based human action recognition benefits from multiple cameras which can provide temporally synchronized, multi-view videos. Cross-video person identification, i.e., determining whether at a given time, persons tracked in different videos are the same person or not, is a key step to integrate such multi-view information for collaborative action recognition. For fixed cameras, this step is relatively easy since different cameras can be pre-calibrated. In this paper, we study cross-video person identification for wearable cameras, which are constantly moving with the wearers. Specifically, we take the tracked persons from different videos to be the same person if their 3D poses are the same, given that these videos are synchronized. We adapt an existing algorithm to estimate the tracked person's 3D poses in each 2D video using motionbased features. Experiments show that, although 3D pose estimation is not perfect, the proposed method can still lead to better cross-video person identification than using appearance information.
Year
DOI
Venue
2016
10.1109/CVPRW.2016.106
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Field
DocType
Volume
Computer vision,Computer science,Wearable computer,Action recognition,3D pose estimation,Artificial intelligence
Conference
2016
Issue
ISSN
Citations 
1
2160-7508
1
PageRank 
References 
Authors
0.35
11
5
Name
Order
Citations
PageRank
Kang Zheng1427.41
Hao Guo2194.03
Xiaochuan Fan3525.01
Hongkai Yu45211.49
Song Wang595479.55