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. © 2009 Springer Berlin Heidelberg.
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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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5414 LNCS, pp. 611–623). https://doi.org/10.1007/978-3-540-92957-4_53
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