Person Re-Identification via Pyramid Multipart Features and Multi-Attention Framework

• Published in: Big data and cognitive computing Vol. 6; no. 1; p. 20
• Main Authors: Bayoumi, Randa Mohamed, Hemayed, Elsayed E., Ragab, Mohammad Ehab, Fayek, Magda B.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2022
• Subjects: Aggregates; Artificial neural networks; attention; Collaboration; computer vision; Datasets; Deep learning; Identification; multi-granularities; Neural networks; Occlusion; person re-identification; Representations; Robustness; temporal aggregation
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc6010020

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.