DocumentCode :
3765473
Title :
Person re-identification using human salience based on multi-feature fusion
Author :
Yang Mingyang;Wan Wanggen;Hou Li;Zhang Yifan
Author_Institution :
School of Communication and Information Engineering, Shanghai University, Shanghai
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
195
Lastpage :
199
Abstract :
Person re-identification plays an important role in matching pedestrians across disjoint camera views. Human salience is distinctive and reliable information in matching, but we will get different results by using different features. In this paper, in order to solve some person reidentification problems, we exploit multi-feature fusion method include RGB, SIFT and Rotation invariant LBP (RI-LBP) to improve the salience feature representation. Due to rotation invariant RI-LBP and SIFT have robust rotation invariant properties, the experiment results are relatively stable. In addition, human salience is also combined with SDALF to improve the performance of person re-identification, and we found a suitable weight between these two methods, which improves the results significantly. Finally, the effectiveness of our approach is validated on the widely used VIPeR dataset, and the experimental results show that our proposed method outperforms most state-of-the-art methods.
Publisher :
iet
Conference_Titel :
Smart and Sustainable City and Big Data (ICSSC), 2015 International Conference on
Type :
conf
DOI :
10.1049/cp.2015.0268
Filename :
7446451
Link To Document :
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