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 language | English |
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Title of host publication | 12th International Conference on Image Analysis and Processing, 2003.Proceedings. |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 01.12.2003 |
Pages | 430-435 |
Article number | 1234088 |
ISBN (Print) | 0-7695-1948-2 |
DOIs | |
Publication status | Published - 01.12.2003 |
Event | 12th International Conference on Image Analysis and Processing - Mantova, Italy Duration: 17.09.2003 → 19.09.2003 Conference number: 101350 |