Vehicle surveillance with a generic, adaptive, 3D vehicle model

  • Leotta M
  • Mundy J
  • 63

    Readers

    Mendeley users who have this article in their library.
  • 41

    Citations

    Citations of this article.

Abstract

In automated surveillance, one is often interested in tracking road vehicles, measuring their shape in 3-d world space, and determining vehicle classification. To address these tasks simultaneously, an effective approach is the constrained alignment of a prior model of 3-d vehicle shape to images. Previous 3-d vehicle models are either generic but overly simple or rigid and overly complex. Rigid models represent exactly one vehicle design, so a large collection is needed. A single generic model can deform to a wide variety of shapes, but those shapes have been far too primitive. This paper uses a generic 3-d vehicle model that deforms to match a wide variety of passenger vehicles. It is adjustable in complexity between the two extremes. The model is aligned to images by predicting and matching image intensity edges. Novel algorithms are presented for fitting models to multiple still images and simultaneous tracking while estimating shape in video. Experiments compare the proposed model to simple generic models in accuracy and reliability of 3-d shape recovery from images and tracking in video. Standard techniques for classification are also used to compare the models. The proposed model outperforms the existing simple models at each task.

Author-supplied keywords

  • Machine vision
  • image recognition
  • image shape analysis
  • road vehicle location monitoring
  • video signal processing

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Matthew J. Leotta

  • Joseph L. Mundy

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free