Pedestrian Retrieval via Part-Based Gradation Regularization in Sensor Networks | Zendy

Shuang Liu | Zendy; Xiaolong Hao | Zendy; Zhong Zhang | Zendy

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Pedestrian Retrieval via Part-Based Gradation Regularization in Sensor Networks

Author(s) -

Shuang Liu,

Xiaolong Hao,

Zhong Zhang

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2854830

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In this paper, we propose a novel label distribution approach named part-based gradation regularization (PGR) for pedestrian retrieval in sensor networks. Considering different importance of various body parts, we present a gradual function to assign pedestrian label for each horizontal part. In this way, we can conduct part-based supervised learning using the identification network. The proposed PGR not only learns the discriminative local convolutional neural network-based features, but also considers the significance of assigning pedestrian label for different horizontal parts. Experimental results show that the proposed PGR obtains better performance than other approaches on three pedestrian retrieval databases, i.e., Market-1501, CUHK03, and DukeMTMC-reID databases.

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