Abstract
This paper discusses the detection of moving objects (being a crucial part of driver assistance systems) using monocular or stereoscopic computer vision. In both cases, object detection is based on motion analysis of individually tracked image points (optical flow), providing a motion metric which corresponds to the likelihood that the tracked point is moving. Based on this metric, points are segmented into objects by employing a globally optimal graph-cut algorithm. Both approaches are comparatively evaluated using real-world vehicle image sequences.
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Klappstein, J., Vaudrey, T., Rabe, C., Wedel, A., Klette, R. (2009). Moving Object Segmentation Using Optical Flow and Depth Information. In: Wada, T., Huang, F., Lin, S. (eds) Advances in Image and Video Technology. PSIVT 2009. Lecture Notes in Computer Science, vol 5414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92957-4_53
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DOI: https://doi.org/10.1007/978-3-540-92957-4_53
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