Fine Pedestrian Segmentation with Parts Detection and Retrieval

被引：0

作者：

Wang F. ^{[1
]}

Li Z. ^{[1
]}

Liu Q.-S. ^{[1
]}

Sun Y.-B. ^{[1
]}

机构：

[1] Collaborative Innovation Center, School of Information & Control, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2019年 / 47卷 / 02期

关键词：

Fast R-CNN; Over-segmentation; Parts retrieval; Pedestrian segmentation;

D O I：

10.3969/j.issn.0372-2112.2019.02.035

中图分类号：

学科分类号：

摘要：

Focused on the diversity of appearance and the complexity of configuration, laying, and occasion in human images, a coarse-to-fine method was proposed for effective human parsing. It can decompose a human image into semantic regions which consists of three phases. In the first two phases, two effective models were trained with Fast Region-based Convolutional Network(Fast R-CNN)to respectively detect human body and clothing items. In the third phase, parsing clothing items based on retrieving similar over-segmented images and morphing them into absolute image coordinates. Experiments are conducted on three public databases, and the experimental results show that proposed method has higher accuracy and promising performance. © 2019, Chinese Institute of Electronics. All right reserved.

引用

页码：502 / 508

页数：6

共 24 条

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