DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

Yi Zhou, Li Liu, Ling Shao, Matt Mellor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

54 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationEuropean Conference on Computer Vision
Subtitle of host publication14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II
Number of pages16
ISBN (Electronic)978-3-319-46475-6
ISBN (Print)978-3-319-46474-9
Publication statusPublished - 17 Sep 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


  • Vehicle Detection
  • Attributes Annotation
  • Latent Knowledge Guidance
  • Joint Learning
  • Deep Networks

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