| Abstract: |  This thesis deals with passenger car recognition in individual frames of cross-traffic video sequences. Firstly, we have suggested characteristic visual features of the car object class that are suitable for detection from the side-view. Secondly, we have designed, implemented and trained detectors of these individual features. Thirdly, we have suggested a structural model of car side-view, which allowed us to integrate the detectors of the individual features together. Finally, we have proposed a probabilistic fusion of the visual features and structural model. The probabilistic fusion is then used for final detection of cars as whole objects.
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