| Title: |
Person Re-Identification via Pyramid Multipart Features and Multi-Attention Framework |
| Authors: |
Randa Mohamed Bayoumi; Elsayed E. Hemayed; Mohammad Ehab Ragab; Magda B. Fayek |
| Source: |
Big Data and Cognitive Computing, Vol 6, Iss 20, p 20 (2022) |
| Publisher Information: |
MDPI AG |
| Publication Year: |
2022 |
| Collection: |
Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: |
computer vision; deep learning; person re-identification; attention; temporal aggregation; multi-granularities; Technology |
| Description: |
Video-based person re-identification has become quite attractive due to its importance in many vision surveillance problems. It is a challenging topic due to the inter/intra changes, occlusion, and pose variations involved. In this paper, we propose a pyramid-attentive framework that relies on multi-part features and multiple attention to aggregate features of multi-levels and learns attention-based representations of persons through various aspects. Self-attention is used to strengthen the most discriminative features in the spatial and channel domains and hence capture robust global information. We propose the use of part-relation attention between different multi-granularities of features’ representation to focus on learning appropriate local features. Temporal attention is used to aggregate temporal features. We integrate the most robust features in the global and multi-level views to build an effective convolution neural network (CNN) model. The proposed model outperforms the previous state-of-the art models on three datasets. Notably, using the proposed model enables the achievement of 98.9% (a relative improvement of 2.7% on the GRL) top1 accuracy and 99.3% mAP on the PRID2011, and 92.8% (a relative improvement of 2.4% relative to GRL) top1 accuracy on iLIDS-vid. We also explore the generalization ability of our model on a cross dataset. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://www.mdpi.com/2504-2289/6/1/20; https://doaj.org/toc/2504-2289; https://doaj.org/article/649524bc3e5b4398808aa2a3aee604b6 |
| DOI: |
10.3390/bdcc6010020 |
| Availability: |
https://doi.org/10.3390/bdcc6010020; https://doaj.org/article/649524bc3e5b4398808aa2a3aee604b6 |
| Accession Number: |
edsbas.D6F23849 |
| Database: |
BASE |