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 detect...Show MoreMetadata
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.
Date of Conference: 17-19 September 2003
Date Added to IEEE Xplore: 29 September 2003
Print ISBN:0-7695-1948-2