Fast automatic vehicle annotation for urban traffic surveillance

Zhou, Yi, Liu, Li, Shao, Ling and Mellor, Matt (2018) Fast automatic vehicle annotation for urban traffic surveillance. IEEE Transactions on Intelligent Transportation Systems, 19 (6). pp. 1973-1984. ISSN 1524-9050

[thumbnail of Accepted manuscript]
PDF (Accepted manuscript) - Accepted Version
Download (5MB) | Preview


Automatic vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for intelligent transportation systems. In this paper, we present a fast algorithm: detection and annotation for vehicles (DAVE), which effectively combines vehicle detection and attributes annotation into a unified framework. DAVE consists of two convolutional neural networks: a shallow fully convolutional fast vehicle proposal network (FVPN) for extracting all vehicles' positions, and a deep attributes learning network (ALN), which aims to verify each detection candidate and infer each vehicle's pose, color, and type information simultaneously. These two nets are jointly optimized so that abundant latent knowledge learned from the deep empirical ALN can be exploited to guide training the much simpler FVPN. Once the system is trained, DAVE can achieve efficient vehicle detection and attributes annotation for real-world traffic surveillance data, while the FVPN can be independently adopted as a real-time high-performance vehicle detector as well. We evaluate the DAVE on a new self-collected urban traffic surveillance data set and the public PASCAL VOC2007 car and LISA 2010 data sets, with consistent improvements over existing algorithms.

Item Type: Article
Uncontrolled Keywords: vehicle detection,attributes annotation,latent knowledge guidance,joint learning,deep networks,sdg 11 - sustainable cities and communities ,/dk/atira/pure/sustainabledevelopmentgoals/sustainable_cities_and_communities
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 14 Nov 2017 06:05
Last Modified: 04 Mar 2024 17:38
DOI: 10.1109/TITS.2017.2740303


Downloads per month over past year

Actions (login required)

View Item View Item