Abstract
Cameras in general are exteroceptive sensors and provide visual information about the local environment. For automotive safety applications, image data is processed to identify critical situations and avoid traffic accidents. Plenoptic cameras which are capable of recording both, angular and spatial information of light, have found increasing interest in optical 3D measurement techniques in recent years. Major advantages are multiple perspective views of captured scene and post focus capability. Therefore, the depth of the scene can be estimated similar to stereo camera.
In this paper, the potential of plenoptic cameras in the field of automotive safety is investigated in three aspects (1) depth estimation performance, (2) feature detection quality, and (3) weather robustness. For comparative results, adverse conditions such as low light or rain are replicated in a test hall. Initial results show that the additional microlens array of plenoptic cameras enable to achieve significant improvements over conventional cameras.
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References
Levoy, M., Hanrahan, P.: Light field rendering. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 31–42. ACM (1996)
Wetzstein, G., Ihrke, I., Lanman, D., Heidrich, W.: State of the art in computational plenoptic imaging. In: EUROGRAPHICS. Citeseer (2010)
Adelson, E.H., Wang, J.Y.: Single lens stereo with a plenoptic camera. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 99–106 (1992)
Hasirlioglu, S., Kamann, A., Doric, I., Brandmeier, T.: Test methodology for rain influence on automotive surround sensors. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 2242–2247. IEEE (2016)
Ng, R.: Fourier slice photography. ACM Trans. Graph. (TOG) 24(3), 735–744 (2005)
Schedl, D.C., Birklbauer, C., Gschnaller, J., Bimber, O.: Generalized depth-of-field light-field rendering. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) ICCVG 2016. LNCS, vol. 9972, pp. 95–105. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46418-3_9
Ng, R., Levoy, M., Bredif, M., Duval, G., Horowitz, M., Hanrahan, P.: Light field photography with a hand-held plenoptic camera. Comput. Sci. Tech. Rep. CSTR 2(11), 1–11 (2005)
Wanner, S., Goldluecke, B.: Variational light field analysis for disparity estimation and super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 606–619 (2014)
Yu, Z., Guo, X., Lin, H., Lumsdaine, A., Yu, J.: Line assisted light field triangulation and stereo matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2792–2799 (2013)
Dansereau, D.G., Pizarro, O., Williams, S.B.: Linear volumetric focus for light field cameras. ACM Trans. Graph. 34(2), 15–1 (2015)
Georgiev, T., Lumsdaine, A.: Reducing plenoptic camera artifacts. Comput. Graph. Forum 29(6), 1955–1968 (2010). Wiley Online Library
Bishop, T.E., Favaro, P.: The light field camera: extended depth of field, aliasing, and superresolution. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 972–986 (2012)
Jeon, H.-G., Park, J., Choe, G., Park, J., Bok, Y., Tai, Y.-W., So Kweon, I.: Accurate depth map estimation from a lenslet light field camera. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1547–1555 (2015)
Isaksen, A., McMillan, L., Gortler, S.J.: Dynamically reparameterized light fields. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 297–306. ACM Press/Addison-Wesley Publishing Co. (2000)
Dansereau, D., Bruton, L.: A 4D frequency-planar IIR filter and its application to light field processing. In: Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS 2003, vol. 4, pp. IV. IEEE (2003)
Georgiev, T., Intwala, C.: Light field camera design for integral view photography. Adobe Technical report (2006)
Dansereau, D.G., Pizarro, O., Williams, S.B.: Decoding, calibration and rectification for lenselet-based plenoptic cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1027–1034 (2013)
Hosni, A., Rhemann, C., Bleyer, M., Rother, C., Gelautz, M.: Fast cost-volume filtering for visual correspondence and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 504–511 (2013)
Kolmogorov, V., Zabih, R.: Multi-camera scene reconstruction via graph cuts. Comput. Vis. ECCV 2002, 8–40 (2002)
Lewis, J.P.: Fast normalized cross-correlation. In: Vision Interface, vol. 10, no. 1, pp. 120–123 (1995)
Acknowledgment
This work is supported under the FH-Impuls program of the German Federal Ministry of Education and Research (BMBF) under Grant No. 13FH7I01IA.
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Hasirlioglu, S., Karthik, M., Riener, A., Doric, I. (2018). Potential of Plenoptic Cameras in the Field of Automotive Safety. In: Kováčiková, T., Buzna, Ľ., Pourhashem, G., Lugano, G., Cornet, Y., Lugano, N. (eds) Intelligent Transport Systems – From Research and Development to the Market Uptake. INTSYS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 222. Springer, Cham. https://doi.org/10.1007/978-3-319-93710-6_18
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