Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (3): 634-644.doi: 10.1007/s11390-019-1932-x

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

PVSS: A Progressive Vehicle Search System for Video Surveillance Networks

Xin-Chen Liu, Member, CCF, IEEE, Wu Liu, Member, CCF, IEEE Hua-Dong Ma*, Fellow, CCF, Senior Member, IEEE, Member, ACM, Shuang-Qun Li   

  1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2018-10-20 Revised:2019-03-22 Online:2019-05-05 Published:2019-05-06
  • Contact: Hua-Dong Ma E-mail:mhd@bupt.edu.cn
  • About author:Xin-Chen Liu received his Ph.D. degree at the Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, in 2018, and B.E. degree in computer science from Northwest Agricultural and Forestry University, Xi'an, in 2011. He received the Best Student Paper Award at ICME in 2016. His research interests include multimedia content analysis and computer vision.
  • Supported by:
    This work was supported by the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (NSFC) under Grant No. 61720106007, the NSFC-Guangdong Joint Fund under Grant No. U1501254, the National Key Research and Development Plan of China under Grant No. 2016YFC0801005, the NFSC under Grant No. 61602049, and the 111 Project under Grant No. B18008.

This paper is focused on the task of searching for a specific vehicle that appears in the surveillance networks. Existing methods usually assume the vehicle images are well cropped from the surveillance videos, and then use visual attributes, like colors and types, or license plate numbers to match the target vehicle in the image set. However, a complete vehicle search system should consider the problems of vehicle detection, representation, indexing, storage, matching, and so on. Besides, it is very difficult for attribute-based search to accurately find the same vehicle due to intra-instance changes in different cameras and the extremely uncertain environment. Moreover, the license plates may be mis-recognized in surveillance scenes due to the low resolution and noise. In this paper, a progressive vehicle search system, named as PVSS, is designed to solve the above problems. PVSS is constituted of three modules:the crawler, the indexer, and the searcher. The vehicle crawler aims to detect and track vehicles in surveillance videos and transfer the captured vehicle images, metadata and contextual information to the server or cloud. Then multi-grained attributes, such as the visual features and license plate fingerprints, are extracted and indexed by the vehicle indexer. At last, a query triplet with an input vehicle image, the time range, and the spatial scope is taken as the input by the vehicle searcher. The target vehicle will be searched in the database by a progressive process. Extensive experiments on the public dataset from a real surveillance network validate the effectiveness of PVSS.

Key words: multi-modal data analysis; progressive search system; vehicle search; video surveillance network;

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