Spatial-Temporal Person Re-Identification

Most of current person re-identification (ReID) methods neglect a spatial-temporal constraint. Given a query image, conventional methods compute the feature distances between the query image and all the gallery images and return a similarity ranked table. When the gallery database is very large in practice, these approaches fail to obtain a good performance due to appearance ambiguity across different camera views. In this paper, we propose a novel two-stream spatial-temporal person ReID (st-ReID) framework that mines both visual semantic information and spatial-temporal information. To this end, a joint similarity metric with Logistic Smoothing (LS) is introduced to integrate two kinds of heterogeneous information into a unified framework. To approximate a complex spatial-temporal probability distribution, we develop a fast Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint, the st-ReID model eliminates lots of irrelevant images and thus narrows the gallery database. Without bells and whistles, our st-ReID method achieves rank-1 accuracy of 98. 1% on Market-1501 and 94. 4% on DukeMTMC-reID, improving from the baselines 91. 2% and 83. 8%, respectively, outperforming all previous state-of-theart methods by a large margin.

Authors

• Guangcong Wang
• Jianhuang Lai
• Peigen Huang
• Xiaohua Xie School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China Guangdong Province Key Laboratory of Information Security Technology, China Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China

Keywords

No keywords are indexed for this paper.