Detection of moving objects using a robust displacement estimation including a statistical error analysis

A new technique for the detection and description of moving objects in natural scenes is presented which is based on an object-oriented, statistical multifeature analysis of video sequences. To cope with the problem that image signal changes can have causes other than object motion, additional features, viz, texture and motion beyond temporal signal differences, are extracted and evaluated in an object-oriented fashion. The adaptation of this method to normal fluctuations of the observed scene is performed by a time-recursive space-variant estimation of the temporal probability distributions of the features. Feature data which differ significantly from the estimated distributions are interpreted to be caused by moving objects. For motion feature extraction, a robust displacement estimation algorithm is applied which is oriented towards the joint estimation of displacement vectors and their corresponding reliability measures. The reliability measures judge object motion and control the alarm setting. The advantages of the presented object detection algorithm compared to mere change detection techniques are demonstrated by some experiments. Due to its capability to automatically learn the observed scene the calibration effort of the sensor is extremely small.