Prediction errors are commonly used when analyzing the performance of a multi-camera stereo system using at least three cameras. This paper discusses this methodology for performance evaluation for the first time on long stereo sequences (in the context of vision-based driver assistance systems). Three cameras are calibrated in an ego-vehicle, and prediction error analysis is performed on recorded stereo sequences. They are evaluated using various common stereo matching algorithms, such as belief propagation, dynamic programming, semi-global matching, or graph cut. Performance is evaluated on both synthetic and real data. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Morales, S., & Klette, R. (2009). A third eye for performance evaluation in stereo sequence analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5702 LNCS, pp. 1078–1086). https://doi.org/10.1007/978-3-642-03767-2_131
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