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.
|Title of host publication||12th International Conference on Image Analysis and Processing, 2003.Proceedings.|
|Number of pages||6|
|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