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2016 | OriginalPaper | Buchkapitel

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

verfasst von : Yi Zhou, Li Liu, Ling Shao, Matt Mellor

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for urban traffic surveillance. In this paper, we present a fast framework of Detection and Annotation for Vehicles (DAVE), which effectively combines vehicle detection and attributes annotation. DAVE consists of two convolutional neural networks (CNNs): a fast vehicle proposal network (FVPN) for vehicle-like objects extraction and an attributes learning network (ALN) aiming to verify each proposal and infer each vehicle’s pose, color and type simultaneously. These two nets are jointly optimized so that abundant latent knowledge learned from the ALN can be exploited to guide FVPN training. Once the system is trained, it can achieve efficient vehicle detection and annotation for real-world traffic surveillance data. We evaluate DAVE on a new self-collected UTS dataset and the public PASCAL VOC2007 car and LISA 2010 datasets, with consistent improvements over existing algorithms.

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“Catch-all” indicates other undefined types and colors which are not included in our training model.

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Titel: DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
verfasst von: Yi Zhou
Li Liu
Ling Shao
Matt Mellor
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46474-9

Electronic ISBN: 978-3-319-46475-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46475-6_18

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