Paper Info
Title
Deep Siamese Network with Multi-level Similarity Perception for Person Re-identification.
Abstract
Person re-identification (re-ID), which aims at spotting a person of interest across multiple camera views, has gained more and more attention in computer vision community. In this paper, we propose a novel deep Siamese architecture based on convolutional neural network (CNN) and multi-level similarity perception. According to the distinct characteristics of diverse feature maps, we effectively apply different similarity constraints to both low-level and high-level feature maps, during training stage. Therefore, our network can efficiently learn discriminative feature representations at different levels, which significantly improves the re-ID performance. Besides, our framework has two additional benefits. Firstly, classification constraints can be easily incorporated into the framework, forming a unified multi-task network with similarity constraints. Secondly, as similarity comparable information has been encoded in the network's learning parameters via back-propagation, pairwise input is not necessary at test time. That means we can extract features of each gallery image and build index in an off-line manner, which is essential for large-scale real-world applications. Experimental results on multiple challenging benchmarks demonstrate that our method achieves splendid performance compared with the current state-of-the-art approaches.
Year
DOI
Venue
2017
10.1145/3123266.3123452
MM '17: ACM Multimedia Conference Mountain View California USA October, 2017
Keywords
Field
DocType
Person Re-identification, Multi-level Similarity Perception, Deep Siamese Architecture, Convolutional Neural Network
Pairwise comparison,Computer vision,Architecture,Computer science,Convolutional neural network,Artificial intelligence,Perception,Spotting,Discriminative model,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-4906-2
10
0.54
References 
Authors
26
7
Name
Order
Citations
PageRank
chen shen110317.21
Zhongming Jin2606.23
Yiru Zhao3604.36
Zhihang Fu4252.77
Rongxin Jiang55910.63
Yaowu Chen617527.78
Xian-Sheng Hua76566328.17