Pose-Guided Part-Based Adaptive Pyramid Features for Occluded Person Reidentification

Xiaobing Lin, Jilin Li, Zengxi Huang and Xiaoqin Tang

Mathematical Problems in Engineering, 2020, vol. 2020, 1-8

Abstract:

Reidentifying an occluded person across nonoverlapping cameras is still a challenging task. In this work, we propose a novel pose-guided part-based adaptive pyramid neural network for occluded person reidentification. Firstly, to alleviate the impact of occlusion, we utilize pose landmarks to generate pose-guided attention maps. The attention maps will help the model focus on the nonoccluded regions. Secondly, we use pyramid pooling to extract multiscale features in order to address the scale variation problem. The generated pyramid features are then multiplied by attention maps to achieve pose-guided adaptive pyramid features. Thirdly, we propose a pose-guided body part partition scheme to deal with the alignment problem. Accordingly, the adaptive pyramid features are divided into partitions and fed into individual fully connected layers. In the end, all the part-based matching scores are fused with a weighted sum rule for person reidentification. The effectiveness of our method is clearly validated by the experimental results on two popular occluded and holistic datasets, i.e., Occluded-DukeMTMC and the Market-1501.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2020/6694670.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2020/6694670.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6694670

DOI: 10.1155/2020/6694670

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().