Abstract
This paper proposes a technique for estimating 3D flow vectors, by combining a KLT tracker with subsequent scale-space analysis of tracked points. A tracked point defines a 2D vector, which is mapped into 3D space based on ratios of maxima of scale-space characteristics. The approach is tested for night-vision sequences as recorded (at Daimler AG, Germany) for driver assistance projects. Those image sequences (at 25Hz) are characterized by being slightly blurry and of low contrast. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Sánchez, J. A., Klette, R., & Destefanis, E. (2009). Estimating 3D flow for driver assistance applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5414 LNCS, pp. 237–248). https://doi.org/10.1007/978-3-540-92957-4_21
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