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Contour extraction of moving objects in complex outdoor scenes

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Abstract

This paper presents a new approach to the extraction of the contour of a moving object. The method is based on the fusion of a motion segmentation technique using image subtraction and a color segmentation technique based on the split-and-merge paradigm and edge information obtained from using the Canny edge detector. The advantages of this method are the following: it can detect large moving objects, the background can be arbitrarily complicated and contain many nonmoving objects, and it requires only three image frames that need not be consecutive provided that the moving object is entirely contained in the three frames. It is assumed that there is only one moving object in the image and the objects are not blurred by their motion so that the edges in the image are sharp. The method was applied to road images containing a moving vehicle, and the results show that the contour was correctly extracted in 18 of the 20 cases. We show that this contour extraction method gives good results for other types of moving objects as well. We also describe how the extracted contour can be used to classify a given vehicle into five generic categories. In this study, 19 out of the 20 vehicles were correctly classified. These results demonstrate that integration of multiple cues obtained from relatively simple image analysis techniques leads to a robust extraction of the object of interest in complex outdoor scenes.

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Authors and Affiliations

  1. Computer Science Department, Michigan State University, 48824, East Lansing, MI

    Marie-Pierre Dubuisson & Anil K. Jain

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  1. Marie-Pierre Dubuisson
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  2. Anil K. Jain
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Research supported by a grant from the U.S. Department of Transportation through the Great Lakes Center for Truck Transportation Research and by a grant from the National Science Foundation (CDA-8806599).

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Dubuisson, MP., Jain, A.K. Contour extraction of moving objects in complex outdoor scenes. Int J Comput Vision 14, 83–105 (1995). https://doi.org/10.1007/BF01421490

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  • DOI: https://doi.org/10.1007/BF01421490

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