DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

Zhou, Yi, Liu, Li, Shao, Ling and Mellor, Matt (2016) DAVE: A Unified Framework for Fast Vehicle Detection and Annotation. In: European Conference on Computer Vision. Lecture Notes in Computer Science, 9906 . Springer, pp. 278-293. ISBN 978-3-319-46474-9

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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.

Item Type:	Book Section
Uncontrolled Keywords:	vehicle detection,attributes annotation,latent knowledge guidance,joint learning,deep networks
Faculty \ School:	Faculty of Science > School of Computing Sciences
Depositing User:	Pure Connector
Date Deposited:	07 Feb 2017 02:42
Last Modified:	22 Oct 2022 00:00
URI:	https://ueaeprints.uea.ac.uk/id/eprint/62337
DOI:	10.1007/978-3-319-46475-6_18

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