| Full text | |
| Author(s): |
Valem, Lucas Pascotti
;
Pedronette, Daniel Carlos Guimaraes
Total Authors: 2
|
| Document type: | Journal article |
| Source: | Image and Vision Computing; v. 123, p. 14-pg., 2022-05-31. |
| Abstract | |
Person Re-ID has been gaining a lot of attention and nowadays is of fundamental importance in many camera sur-veillance applications. The task consists of identifying individuals across multiple cameras that have no overlap-ping views. Most of the approaches require labeled data, which is not always available, given the huge amount of demanded data and the difficulty of manually assigning a class for each individual. Recently, studies have shown that re-ranking methods are capable of achieving significant gains, especially in the absence of labeled data. Be-sides that, the fusion of feature extractors and multiple-source training is another promising research direction not extensively exploited. We aim to fill this gap through a manifold rank aggregation approach capable of exploiting the complementarity of different person Re-ID rankers. In this work, we perform a completely unsu-pervised selection and fusion of diverse ranked lists obtained from multiple and diverse feature extractors. Among the contributions, this work proposes a query performance prediction measure that models the relation-ship among images considering a hypergraph structure and does not require the use of any labeled data. Expres-sive gains were obtained in four datasets commonly used for person Re-ID. We achieved results competitive to the state-of-the-art in most of the scenarios.(c) 2022 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |
| FAPESP's process: | 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |