Abstract
Many heterogeneous car video recorders are sold in the market. In order to record the ultra-wide-angle road scenes, at least two heterogeneous recorders are used. These heterogeneous recorders have different viewing angles, different resolutions, and different lens sensors. Because of different hardware and software of the heterogeneous recorders, the captured videos are heterogeneous, which is very challenging for video stitching research. The traditional image stitching system includes color correction, feature detection, feature descriptor, feature matching, and video stitching. When a traditional image stitching system is used to process the images captured by homogeneous cameras, the results are reasonably good. However, when a traditional image stitching system is used to process the images captured by heterogeneous cameras, the results are not so good. Furthermore, applying a traditional image stitching system to process videos captured by heterogeneous car video recorders is time-consuming. This paper presents a study that tested multiple existing methods to evaluate their capability when used to stitch heterogeneous images to allow a driver to see an ultra-wide-angle driving view without blind spots and to record these images. Experimental results show that some methods used in this study can be used for this purpose, but they have significant error rates and are time-consuming.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blind spot. http://en.wikipedia.org/wiki/Blind_spot_(vehicle)
Traffic collision. http://en.wikipedia.org/wiki/Traffic_collision
A Plus Pillar concept gives eyes to a driver’s blind spot. http://www.damngeeky.com/2012/10/30/6751/a-plus-pillar-concept-gives-eyes-to-a-drivers-blind-spot.html
Tsai, C.M.: Non-motion blur detection for helping blind persons to ``see’’ business cards. ICMLC 2012, 1901–1906 (2012)
Tsai, C.M.: Text detection in moving business cards for helping visually impaired persons using a wearable camera, IJWMIP, 12:01 (2014) 1450010-1–1450010-16
Levine, M.D., Nazif, A.M.: Dynamic measurement of computer generated image segmentations. IEEE Trans. on PAMI 7(2), 155–164 (1985)
Ng, W.S., Lee, C.K.: Comment on using the uniformity measure for performance measure in image segmentation. IEEE Trans. on PAMI 18(9), 933–934 (1996)
Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. SMC 9, 62–66 (1979)
Tsai, C.M., Yeh, Z.M.: Contrast enhancement by automatic and parameter-free piecewise linear transformation for color images. IEEE Trans. on CE 54(2), 213–219 (2008)
Tsai, C.M., Yeh, Z.M.: Contrast compensation by fuzzy classification and image illumination analysis for back-lit and front-lit color face images. IEEE Trans. on CE 56(3), 1570–1578 (2010)
Tsai, C.M., Yeh, Z.M., Wang, Y.F.: Decision tree-based contrast enhancement for various color images. MVA 22(1), 21–37 (2011)
Tsai, C.M.: Adaptive local power-law transformation for color image enhancement. Appl. Math. Inf. Sci. 7(5), 2019–2026 (2013)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-Up Robust Features (SURF). CVIU 110(3), 346–359 (2008)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. ICCV 2011, 2564–2571 (2011)
Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: Binary Robust Invariant Scalable Keypoints. ICCV 2011, 2548–2555 (2011)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. of the ACM 24(6), 381–395 (1981)
Choi, S., Kim, T., Yu, W.: Robust video stabilization to outlier motion using adaptive ransac. Proceedings of IEEE/RSJ ICIRS 2009, 1897–1902 (2009)
Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application (VISAPP 2009), pp. 331–340 (2009)
Muja, M., Lowe, D.G.: Fast matching of binary features. In: Proceedings of the 2012 9th Conference on Computer and Robot Vision, pp. 404–410 (2012)
Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. on PAMI 36(11), 2227–2240 (2014)
OpenCV (Open source computer vision). http://opencv.org/
Hartley, R.I. and Zisserman, A.: Multiple View Geometry in Computer Vision, second edition. Cambridge University Press (2004)
Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Tseng, TH., Tsai, CM. (2015). Video Stitching System of Heterogeneous Car Video Recorders. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-19066-2_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19065-5
Online ISBN: 978-3-319-19066-2
eBook Packages: Computer ScienceComputer Science (R0)