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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References
Anandan, P.: A Computational Framework and an Algorithm for the Measurement of Visual Motion. Int. J. on Comp. Vision 2, 283–310 (1989)
Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–77 (1994)
Bober, M., Kittler, J.: Estimation of Complex Multimodal Motion: An Approach Based on Robust Statistics and Hough transform. Image and Vision Computing 12, 661–668 (1994)
Coifman, B., Beymer, D., McLauchlan, P., Malik, J.: A Real-Time Computer Vision System for Vehicle Tracking and Traffic Surveillance. Transportation Research: Part C 6(4), 271–288 (1998)
Di Stefano, L., Viarani, E.: Vehicle Detection and Tracking Using the Block Matching Algorithm. In: Proc. of 3rd IMACS/IEEE Int’l Multiconference on Circuits, Systems, Communications and Computer, vol. 1, pp. 4491–4496 (1999)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1993)
Hill, L., Vlachos, T.: Optimal Search in Hough Parameter Hyperspace For Estimation of Complex Motion in Image Sequences. IEE Proc.-Vis. Image Signal Process 149(2), 63–71 (2002)
Illingworth, J., Kittler, J.: A Survey of the Hough Transform. Computer Vision, Graphics, Image Processing 44, 87–116 (1988)
Jain, R.C.: Difference and Accumulative Difference Pictures in Dynamic Scene Analysis. Image and Vision Computing 12(2), 99–108 (1984)
Kang, E.-Y., Cohen, I., Medioni, G.: Non-Iterative Approach to Multiple 2D Motion Estimation. In: Int. Conf. on Pattern Recognition (ICPR 2004), vol. 4, pp. 791–794 (2004)
Nicolescu, M., Medioni, G.: A Voting-Based Computational Framework for Visual Motion Analysis and Interpretation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 739–752 (2005)
Salvador, I., Andreu, G., Pérez, A.: Detection of identifier code in containers. In: IX Spanish Symposium on Pattern Recognition and Image Analysis, vol. 2, pp. 119–124 (2001)
Salvador, I., Andreu, G., Pérez, A.: Preprocessing and Recognition of Characters in Containers Codes. In: Proceeding of the International Conference on Pattern Recognition (ICPR-2002), pp. 101–105 (2002)
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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
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
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