Abstract
This paper presents a method for accurately segmenting moving container trucks in image sequences. This task allows to increase the performance of a recognition system that must identify the container code in order to check the entrance of containers through a port gate. To achieve good tolerance to non uniform backgrounds and the presence of multiple moving containers, an optical flow-based strategy is proposed. The algorithm introduces a voting strategy to detect the largest planar surface that shows a uniform motion of advance. Then, the top and rear limits of this surface are detected by a fast and effective method that searches for the limit that maximizes some object / non-object ratios. The method has been tested offline with a set of pre-recorded sequences, achieving satisfactory results.
This work has been partially supported by grant CICYT DPI2003-09173-C02-01.
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Atienza, V., Rodas, Á., Andreu, G., Pérez, A. (2005). Optical Flow-Based Segmentation of Containers for Automatic Code Recognition. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_70
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DOI: https://doi.org/10.1007/11551188_70
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