TY - GEN
T1 - Multi-Pose Learning based Head-Shoulder Re-identification
AU - Li, Jia
AU - Zhai, Yunpeng
AU - Wang, Yaowei
AU - Shi, Yemin
AU - Tian, Yonghong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - The whole body of person is probably invisible in video surveillance because of occlusion and view angles (such as in crowded public places), on which occasion conventional person re-identification (i.e., whole-body based Re-ID) approaches may not work. To address this problem, we propose a novel deep pairwise model based on multi-pose learning (MPL) which aims at head-shoulder part instead of the whole body. The proposed method explicitly tackles pose variations by learning an ensemble verification conditional probability distribution about relationship among multiple poses. To facilitate the research on this problem, we contribute three head-shoulder datasets based on CUHK03, CUHK01 and VIPeR. Experiments on these datasets demonstrate that our proposed method achieves the state-of-the-art performance.
AB - The whole body of person is probably invisible in video surveillance because of occlusion and view angles (such as in crowded public places), on which occasion conventional person re-identification (i.e., whole-body based Re-ID) approaches may not work. To address this problem, we propose a novel deep pairwise model based on multi-pose learning (MPL) which aims at head-shoulder part instead of the whole body. The proposed method explicitly tackles pose variations by learning an ensemble verification conditional probability distribution about relationship among multiple poses. To facilitate the research on this problem, we contribute three head-shoulder datasets based on CUHK03, CUHK01 and VIPeR. Experiments on these datasets demonstrate that our proposed method achieves the state-of-the-art performance.
KW - Head Shoulder Re identification
KW - Multi Pose Learning
KW - Pairwise Model
UR - https://www.scopus.com/pages/publications/85050092672
U2 - 10.1109/MIPR.2018.00057
DO - 10.1109/MIPR.2018.00057
M3 - 会议稿件
AN - SCOPUS:85050092672
T3 - Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
SP - 238
EP - 243
BT - Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018
Y2 - 10 April 2018 through 12 April 2018
ER -