Abstract
Rain and snow are often imaged as brighter streaks, which can not only confuse human vision but degrade efficiency of computer vision algorithm. Rain removal is very important technique in these fields such as video-surveillance and automatic driving. Most existing methods rely on optical flow algorithm to detect rain pixel and estimate motion field. However, it is extremely challenging for them to achieve real-time performance. In this paper, a LIDAR based algorithm is proposed, which is capable of achieving rain pixel robustly and efficiently from motion field. The motion objects (vehicles and human) are identified for separation by LIDAR (Sick LMS200) in this paper. Then rain pixels on moving objects are removed by bilateral filter which can preserve edge information instead of causing blurring artifacts around rain streaks. Experimental results show that our method significantly outperforms the previous methods in removing rain pixel and detecting motion objects from motion field.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48(3), 233–254 (2002)
Garg, K., Nayar, S.K.: Detection and removal of rain from videos. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 1. IEEE (2004)
Barnum, P., Kanade, T., Narasimhan, S.: Spatio-temporal frequency analysis for removing rain and snow from videos. In: Proceedings of the First International Workshop on Photometric Analysis for Computer Vision-PACV 2007. INRIA (2007)
Barnum, P.C., Narasimhan, S., Kanade, T.: Analysis of rain and snow in frequency space. Int. J. Comput. Vis. 86(2–3), 256–274 (2010)
Kim, J.-H., et al.: Single-image deraining using an adaptive nonlocal means filter. In: 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE (2013)
Liu, P., Xu, J., Liu, J., et al.: A rain removal method using chromatic property for image sequence. In: 11th Joint International Conference on Information Sciences. Atlantis Press (2008)
Chen, J., Chau, L.P.: A rain pixel recovery algorithm for videos with highly dynamic scenes. IEEE Trans. Image Process. 23(3), 1097–1104 (2014)
Ding, X., Chen, L., Zheng, X., et al.: Single image rain and snow removal via guided L0 smoothing filter. Multimedia Tools Appl. 75, 2697–2712 (2015)
Shen, M., Xue, P.: A fast algorithm for rain detection and removal from videos. In: 2011 IEEE International Conference on Multimedia and Expo (ICME). IEEE (2011)
Tan, C.-H., Chen, J., Chau, L.-P.: Dynamic scene rain removal for moving cameras. In: 2014 19th International Conference on Digital Signal Processing (DSP). IEEE (2014)
Santhaseelan, V., Asari, V.K.: Utilizing local phase information to remove rain from video. Int. J. Comput. Vis. 112(1), 71–89 (2015)
Arras, K.O., Mozos, ó.M., Burgard, W.: Using boosted features for the detection of people in 2D range data. In: 2007 IEEE International Conference on Robotics and Automation. IEEE (2007)
Kurnianggoro, L., Hernandez, D.C., Jo, K.-H.: Camera and laser range finder fusion for real-time car detection. In: 40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014. IEEE (2014)
Acknowledgement
This work was supported by a grant from National Natural Science Foundation of China (NSFC, No. 61504032).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Y., Fu, F., Shi, J., Xu, W., Wang, J. (2016). Efficient Moving Objects Detection by Lidar for Rain Removal. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_64
Download citation
DOI: https://doi.org/10.1007/978-3-319-42297-8_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42296-1
Online ISBN: 978-3-319-42297-8
eBook Packages: Computer ScienceComputer Science (R0)