.png){kind=icon}
.png){kind=icon}
Return
Context-Aware Hypergraph Modeling for Re-identification and Summarization
.png){kind=icon}
OA
AI
DOI：10.1109/TMM.2015.2496139.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Tracking and re-identification in wide-area camera networks is a challenging problem due to non-overlapping visual fields, varying imaging conditions, and appearance changes. We consider the problem of person re-identification and tracking, and propose a novel clothing context-aware color extraction method that is robust to such changes. Annotated samples are used to learn color drift patterns in a non-parametric manner using the random forest distance (RFD) function. The color drift patterns are automatically transferred to associate objects across different views using a unified graph matching framework. A hypergraph representation is used to link related objects for search and re-identification. A diverse hypergraph ranking technique is proposed for person-focused network summarization. The proposed algorithm is validated on a wide-area camera network consisting of ten cameras on bike paths. Also, the proposed algorithm is compared with the state of the art person re-identification algorithms on the VIPeR dataset [1].
Keywords:
Camera network
person re-identification
search
summarization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.7
Papers:
4.5K
Citations:
2.4W
