article

Automatic vehicle recognition in multiple cameras for video surveillance

Author:

Yunbo RaoAuthors Info & Claims

The Visual Computer: International Journal of Computer Graphics, Volume 31, Issue 3

Pages 271 - 280

https://doi.org/10.1007/s00371-013-0917-y

Published: 01 March 2015 Publication History

Abstract

To efficiently locate identical objects in heterogeneous cameras and possibly propagate reliable information between cameras and refine detection, many techniques were used to recognize vehicles. In this paper, we investigate several key problems and present a novel approach for automatic vehicle recognition (AVR) in multiple cameras for video surveillance application. We propose a level-based region comparison algorithm to AVR in multiple cameras. For improving the recognition accuracy, new license plate recognition method is also proposed. Experimental results show that the proposed algorithm is simple and efficient, and the quality of the composed image can be comparable with the results of the state-of-the-art methods.

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Cited By

Gonçalves GDiniz MLaroca RMenotti DSchwartz W(2019)Multi-task Learning for Low-Resolution License Plate RecognitionProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-33904-3_23(251-261)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-33904-3_23
Kumar DBezdek JRajasegarar SLeckie CPalaniswami M(2017)A visual-numeric approach to clustering and anomaly detection for trajectory dataThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-015-1192-x33:3(265-281)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s00371-015-1192-x
Zhang XZhang XWang YYu H(2016)Extended social force model‐based mean shift for pedestrian tracking under obstacle avoidanceIET Computer Vision10.1049/iet-cvi.2016.002211:1(1-9)Online publication date: 16-Nov-2016
https://dl.acm.org/doi/10.1049/iet-cvi.2016.0022
Show More Cited By

Automatic vehicle recognition in multiple cameras for video surveillance
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

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cover image The Visual Computer: International Journal of Computer Graphics

The Visual Computer: International Journal of Computer Graphics Volume 31, Issue 3

March 2015

117 pages

ISSN:0178-2789

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Copyright © Copyright © 2015 Springer-Verlag Berlin Heidelberg.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 March 2015

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Cited By

Gonçalves GDiniz MLaroca RMenotti DSchwartz W(2019)Multi-task Learning for Low-Resolution License Plate RecognitionProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-33904-3_23(251-261)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-33904-3_23
Kumar DBezdek JRajasegarar SLeckie CPalaniswami M(2017)A visual-numeric approach to clustering and anomaly detection for trajectory dataThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-015-1192-x33:3(265-281)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s00371-015-1192-x
Zhang XZhang XWang YYu H(2016)Extended social force model‐based mean shift for pedestrian tracking under obstacle avoidanceIET Computer Vision10.1049/iet-cvi.2016.002211:1(1-9)Online publication date: 16-Nov-2016
https://dl.acm.org/doi/10.1049/iet-cvi.2016.0022
Ben Hamida AKoubaa MNicolas HAmar C(2016)Video surveillance system based on a scalable application-oriented architectureMultimedia Tools and Applications10.1007/s11042-015-2987-575:24(17187-17213)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s11042-015-2987-5

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