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Erschienen in:
An Evolution Perception Mechanism for Organizing and Displaying 3D Shape Collections Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

End-to-End View-Aware Vehicle Classification via Progressive CNN Learning

verfasst von : Jiawei Cao, Wenzhong Wang, Xiao Wang, Chenglong Li, Jin Tang

Erschienen in: Computer Vision

Verlag: Springer Singapore

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Abstract

This paper investigates how to perform robust vehicle classification in unconstrained environments, in which appearance of vehicles changes dramatically across different angles and the numbers of viewpoint images are not balanced among different car models. We propose a end-to-end progressive learning framework, which allows the network architecture is reconfigurable, for view-aware vehicle classification. In particular, the proposed network architecture consists of two parts: a general end-to-end progressive CNN architecture for coarse-to-fine or top-down fine-grained recognition task and an end-to-end view-aware vehicle classification framework to combine vehicle classification and viewpoints recognition. We test the technique on a large-scale car dataset, “CompCars”, and experimental results show that our framework can significantly improve performance of vehicle classification.

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Vorheriges Kapitel Efficient Human Motion Transition via Hybrid Deep Neural Network and Reliable Motion Graph Mining
Nächstes Kapitel A Fast Method for Scene Text Detection
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Metadaten
Titel
End-to-End View-Aware Vehicle Classification via Progressive CNN Learning
verfasst von
Jiawei Cao
Wenzhong Wang
Xiao Wang
Chenglong Li
Jin Tang
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Computer Vision

Print ISBN: 978-981-10-7298-7

Electronic ISBN: 978-981-10-7299-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-7299-4_61