.png){kind=icon}
.png){kind=icon}
Return
Weight-based sparse coding for multi-shot person re-identification
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-015-5404-9.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Person re-identification (Re-ID) is the problem of matching a person from different cameras based on appearance. It has interesting algorithm challenges and extensive practical applications. This paper presents a weight-based sparse coding approach for person re-identification. First, three hypotheses are introduced to achieve a linear combination of images based on sparse coding. Then, we convert the person re-identification problem into an optimization problem with sparse constraints. To reduce the influence of abnormal residuals caused by occlusion and body variation, a weight-based sparse coding approach is proposed to achieve the optimal weights by the ordering statistics of square residuals iteratively. Experiments on various public datasets for different multi-shot modalities have shown good performance of the proposed approach compared with other state-of-the-art ones (more than 42% and 34% at rank-1 on CAVIAR4REID and i-LIDS, respectively).
Keywords:
smart city
person re-identification
video surveillance
multi-shot
weight-based sparse coding
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
4.9K
Citations:
8.9K
