Abstract
In the context of automotive driver assistance, we focus on object detection problem considering data acquired by an on-board stereo pair of cameras. The proposed approach is based on a two-level a-contrario model previously in the context of a fixed camera. In this study, the movement of the camera makes necessary the prediction of the current frame to the following instant. The objects are then detected at a window level as exceptional occurrences of clusters of also exceptional occurrences of significantly high pixel values in the image representing the difference with the predicted image from the previous frame. The term ‘exceptional realizations’ refers to a ‘naive’ model describing roughly the absence of objects. We show that such an approach is successful even when the movement of the camera is only approximately known, since the optimization of our criterion provides also the precise movement. Results on simulated and real data illustrate these statements.
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Ammar, M., Le Hégarat-Mascle, S., Vasiliu, M., Mounier, H. (2012). An A-contrario Approach for Obstacle Detection in Assistance Driving Systems. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_46
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DOI: https://doi.org/10.1007/978-3-642-31295-3_46
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