Person Re-Identification Using Human Salience Based On Multi-Feature Fusion
Person Re-Identification Using Human Salience Based On Multi-Feature Fusion
- Author(s): Yang Mingyang ; Wan Wanggen ; Hou Li ; Zhang Yifan
- DOI: 10.1049/cp.2015.0268
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
2015 International Conference on Smart and Sustainable City and Big Data (ICSSC) — Recommend this title to your library
Thank you
Your recommendation has been sent to your librarian.
- Author(s): Yang Mingyang ; Wan Wanggen ; Hou Li ; Zhang Yifan Source: 2015 International Conference on Smart and Sustainable City and Big Data (ICSSC), 2015 page ()
- Conference: 2015 International Conference on Smart and Sustainable City and Big Data (ICSSC)
- DOI: 10.1049/cp.2015.0268
- ISBN: 978-1-78561-032-5
- Location: Shanghai, China
- Conference date: 26-27 July 2015
- Format: PDF
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.
Inspec keywords: pedestrians; image representation; transforms; image colour analysis; sensor fusion
Subjects: Integral transforms; Computer vision and image processing techniques; Traffic engineering computing; Sensor fusion; Integral transforms; Optical, image and video signal processing
Related content
content/conferences/10.1049/cp.2015.0268
pub_keyword,iet_inspecKeyword,pub_concept
6
6