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

Daniel Toth, Til Aach

Abstract

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.

Original languageEnglish
Title of host publication 12th International Conference on Image Analysis and Processing, 2003.Proceedings.
Number of pages6
PublisherIEEE
Publication date01.12.2003
Pages430-435
Article number1234088
ISBN (Print)0-7695-1948-2
DOIs
Publication statusPublished - 01.12.2003
Event12th International Conference on Image Analysis and Processing
- Mantova, Italy
Duration: 17.09.200319.09.2003
Conference number: 101350

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