[1]LI Long,Yin Hui,XU Hongli,et al.A robust multi-object detection and matching algorithmfor multi-egocentric videos[J].CAAI Transactions on Intelligent Systems,2016,11(5):619-626.[doi:10.11992/tis.201603050]
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A robust multi-object detection and matching algorithmfor multi-egocentric videos

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 5 Page number: 619-626 Column: 学术论文—机器学习 Public date: 2016-11-01
Title:
A robust multi-object detection and matching algorithmfor multi-egocentric videos
Author(s):
LI Long1;  Yin Hui1; 2;  XU Hongli1;  OU Weiqi1
1. Department of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China;
2. Beijing Key Lab of Transportation Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
Keywords:
multi-egocentric video; multi-object detection; multi-object matching
CLC:
TP391.4
DOI:
10.11992/tis.201603050
Abstract:
In this paper, a robust multi-object detection and matching algorithm for a multi-egocentric video is proposed by considering the characteristics of multi-egocentric videos, for example, sudden changes in background, and variable target scales and viewpoints. First, a multi-target detection model based on a boosting method is constructed, to roughly detect any salient objects in the video frames. Then an optimization algorithm based on local similarity is proposed for optimizing the salient-object area and improving the accuracy of salient-object detection and localization. Finally, a SVM classifier based on HOG features is trained to realize multi-target matching in multi-egocentric videos. Experiments using Scene Party datasets show the effectiveness of the proposed method.
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