Hierarchical Scheme of Vehicle Detection and Tracking in Nighttime Urban Environment

In this paper, we propose a novel hierarchical scheme for detection and tracking of vehicles using a vehicle-mounted camera in nighttime under urban environment, where a vehicle can be represented by a pair of taillights and various types of lights are commonplace. The proposed scheme, therefore, mainly focuses on devising robust detection and pairing of taillights in spite of their inherent diversity and continuous transformation in appearance. Thus the appearance symmetry, which many conventional methods rely on, for paring is not guaranteed to be available all the times. Each of the three layers in the scheme is devised to identify a vehicle from individual lights and clutters detected in a hierarchical manner. Robust detection of a pair of taillights, which can be regarded as a vehicle, is sought by successive groupings of the components in a layer and checking not only the intra-layer but the inter-layer relations between them. A structural Kalman filter is employed to maintain the temporal consistency in the motion of the components and their relations as well. Exploiting such relational information increases accuracy in tracking of individual components by reducing effects from fluctuation in positions and shapes, and eventually compensating possible failures in detection of them. As a result, the proposed scheme achieves enhancement in detection and tracking of vehicles in nighttime as proven by experiments on videos including crowded urban traffic scenes.

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    • Copyright © 2018, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg. The contents of this paper reflect the views of the authors and do not necessarily reflect the official views or policies of the Transportation Research Board or the National Academy of Sciences.
  • Authors:
    • Lee, Hyug Jae
    • Moon, Byeungjun
    • Kim, Gyeonghwan
  • Publication Date: 2018-4

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  • English

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  • Accession Number: 01665808
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 28 2018 9:47AM