A computer vision system in an autonomous vehlcle guidance application is presented for interpreting image sequences acquired by a camera moving relative to the environment. Objects with different shapes and changing positions as well as motion parameters in the perceived scene have to be recognized even if they occlude each other. The approach described is based on checking hypotheses by a combination of methods from knowledge representation and from control theory, e.g. recursive estimation. Hypothesis verifica-tion is done by analysing the estimated motion parame-ters using methods from statistics. These algorithms have been implemented and tested on synthetic images. Tests using noise corrupted measurements from a CCD-camera are currently performed. Keywords Computer vision, 3D-object recognition, occlusion, hy-CCD-chlp. Also occlusions may result from the move-ment of the camera relative to the surrounding environ-ment or from autonomous moving objects, e.g. cars overtaking each other. The research work discussed here deals with occlusions arising from situations of overtaking cars on German motorways. But it should be no problem to adapt the algorithms to different situa-tions. Figure 1 shows a synthetic image of a German standard "Autobahn " scene generated by a graphic-workstation with two cars (similar to trucks) driving in front of the ego-car causing occlusions. 'Qt' pothesis generation and verification, recursive estima- / tion L-_____ _ ~ _______________ _ ~ I
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