Evaluation of Local Features Using Convolutional Neural Networks for Person Re-Identification

被引：0
|
作者：
Liu, Shuang [1 ,2 ]
Hao, Xiaolong [1 ,2 ]
Zhang, Zhong [1 ,2 ]
Shi, Mingzhu [1 ,2 ]
机构：
[1] Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tr, Tianjin, Peoples R China
[2] Tianjin Normal Univ, Coll Elect & Commun Engn, Tianjin, Peoples R China
来源：
COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS, CSPS 2018, VOL II: SIGNAL PROCESSING | 2020年 / 516卷
基金：
中国国家自然科学基金;
关键词：
Local features; Convolutional neural networks; Person re-identification;
D O I：
10.1007/978-981-13-6504-1_107
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In this paper, we mainly evaluate the influence of local features extracted by convolutional neural networks for person re-identification. Considering the variant body parts with different structural information, we divide the holistic person images into several parts and extract their features. Two kinds of aggregation methods are used to aggregate local features. Experiments on the challenging person re-identification database, Market-1501 database, show that the max aggregation is more effective for extracting the discriminative local features than the sum aggregation.
引用
收藏
页码：890 / 897
页数：8
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