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Anomalous Trajectory Detection Between Regions of Interest Based on ANPR System

  • Gao Ying
  • Nie Yiwen
  • Yang Wei
  • Xu Hongli
  • Huang Liusheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

Abstract

With the popularization of automobiles, more and more algorithms have been proposed in the last few years for the anomalous trajectory detection. However, existing approaches, in general, deal only with the data generated by GPS devices, which need a great deal of pre-processing works. Moreover, without the consideration of region’s local characteristics, those approaches always put all trajectories even though with different source and destination regions together. Therefore, in this paper, we devise a novel framework for anomalous trajectory detection between regions of interest by utilizing the data captured by Automatic Number-Plate Recognition (ANPR) system. Our framework consists of three phases: abstraction, detection, classification, which is specially engineered to exploit both spatial and temporal features. In addition, extensive experiments have been conducted on a large-scale real-world datasets and the results show that our framework can work effectively.

Keywords

Anomalous trajectory Regions of interest ANPR system 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Gao Ying
    • 1
  • Nie Yiwen
    • 1
  • Yang Wei
    • 1
  • Xu Hongli
    • 1
  • Huang Liusheng
    • 1
  1. 1.University of Science and Technology of ChinaHefeiChina

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