Title
Fast and accurate person re-identification with xception conv-net and C2F
Author
van Rooijen, A.L.
Bouma, H.
Verbeek, F.
Contributor
Morales, J. (editor)
Vera-rodriguez, A. (editor)
Fierrez, R. (editor)
Publication year
2019
Abstract
Person re-identification (re-id) is the task of identifying a person of interest across disjoint camera views in a multi-camera system. This is a challenging problem due to the different poses, viewpoints and lighting conditions. Deeply learned systems have become prevalent in the person re-identification field as they are capable to deal with the these obstacles. Conv-Net using a coarse-to-fine search framework (Conv-Net+C2F) is such a deeply learned system, which has been developed with both a high-retrieval accuracy as a fast query time in mind. We propose three contributions to improve Conv-Net+C2F: (1) training with an improved optimizer, (2) constructing Conv-Net using a different Convolutional Neural Network (CNN) not yet used for person re-id and (3) coarse descriptors having fewer dimensions for improved speed as well as increased accuracy. With these adaptations Xception Conv-Net+C2F achieves state-of-the-art results on Market-1501 (single-query, 72.4% mAP) and the new, challenging data split of CUHK03 (detected, 42.6% mAP).
Subject
Convolutional neural networks
Feature extraction
Image retrieval
Large-scale person retrieval
Person re-identification
Cameras
Convolution
Feature extraction
Image retrieval
Neural networks
Coarse-to-fine searches
Convolutional neural network
Large-scale person retrieval
Lighting conditions
Multicamera systems
Person re identifications
Retrieval accuracy
State of the art
Pattern recognition
To reference this document use:
http://resolver.tudelft.nl/uuid:77a231f5-e226-4bdf-a8b8-43b9a7193447
TNO identifier
865906
Publisher
Springer Verlag
ISBN
9783030134686
ISSN
0302-9743
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, 19 November 2018 through 22 November 2018, 611-619
Document type
conference paper