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Volume 34 Issue 3
May  2019
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Xin-Chen Liu, Wu Liu, Hua-Dong Ma, Shuang-Qun Li. PVSS: A Progressive Vehicle Search System for Video Surveillance Networks[J]. Journal of Computer Science and Technology, 2019, 34(3): 634-644. DOI: 10.1007/s11390-019-1932-x
Citation: Xin-Chen Liu, Wu Liu, Hua-Dong Ma, Shuang-Qun Li. PVSS: A Progressive Vehicle Search System for Video Surveillance Networks[J]. Journal of Computer Science and Technology, 2019, 34(3): 634-644. DOI: 10.1007/s11390-019-1932-x
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PVSS: A Progressive Vehicle Search System for Video Surveillance Networks

  • Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China

Funds: 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.
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  • Author Bio:

    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.

  • Corresponding author:

    Hua-Dong Ma E-mail: mhd@bupt.edu.cn

  • Received Date: October 19, 2018
  • Revised Date: March 21, 2019
  • Published Date: May 04, 2019
    Abstract
  • 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.
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    • References (44)
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    3. Zhedong Zheng, Tao Ruan, Yunchao Wei, et al. VehicleNet: Learning Robust Visual Representation for Vehicle Re-Identification. IEEE Transactions on Multimedia, 2021, 23: 2683. DOI:10.1109/TMM.2020.3014488
    4. Qi Liu, Song-Lu Chen, Zhen-Jia Li, et al. Document Analysis and Recognition – ICDAR 2021. Lecture Notes in Computer Science, DOI:10.1007/978-3-030-86337-1_9
    5. Eleni Kamenou, Jesus Martinez del Rincon, Paul Miller, et al. Multi-level Deep Learning Vehicle Re-identification using Ranked-based Loss Functions. 2020 25th International Conference on Pattern Recognition (ICPR), DOI:10.1109/ICPR48806.2021.9412415
    6. Melike Sah, Cem Direkoglu. LightWeight Deep Convolutional Neural Networks for Vehicle Re-Identification using Diffusion-based Image Masking. 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), DOI:10.1109/HORA52670.2021.9461345
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