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

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 feed–forward neural net is used to distinguish between humans, vehicles, and background clutters.

• Type: Conference
• Year: 2003
• Where: ICIAP
• Authors: Daniel Toth, Til Aach
• Keywords: ICIAP 2003, Illumination Invariant Motion, Image Processing, Object Classes, Object Recognition