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Detection and recognition of moving objects using statistical motion detection and Fourier descriptors

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2 Author(s)
Toth, D. ; Inst. for Signal Process., Univ. of Luebeck, Lubeck, Germany ; Aach, T.

Object recognition, i.e. classification of objects into one of several known object classes, generally is a difficult task. In this paper we address the problem of detecting and classifying moving objects in image sequences from traffic scenes recorded with a static camera. In the first step, a statistical, illumination invariant motion detection algorithm is used to produce binary masks of the scene-changes. Next, Fourier descriptors of the shapes from the refined masks are computed and used as feature vectors describing the different objects in the scene. Finally, a feedforward neural net is used to distinguish between humans, vehicles, and background clutter.

Published in:

Image Analysis and Processing, 2003.Proceedings. 12th International Conference on

Date of Conference:

17-19 Sept. 2003