A 3D neural model for video analysis

We propose a 3D self organizing neural model for modeling both the background and the foreground in video, helping in distinguishing between moving and stopped objects in the scene. Our aim is to detect foreground objects in digital image sequences taken from stationary cameras and to distinguish them into moving and stopped objects by a model based approach. We show through experimental results that a good discrimination can be achieved for color video sequences that represent typical situations critical for vehicles stopped in no parking areas. © 2009 The authors and IOS Press. All rights reserved.