Description

ABSTRACT

Advanced driver assistance systems (ADASs) based on video cameras are becoming pervasive in today’s automotive industry. However, while most of these systems perform nicely in clear weather conditions, their performances fail drastically in adverse weather and particularly in the rain. We present two novel approaches that aim to detect unfocused raindrops on a car windshield using only images from an in-vehicle camera. Based on the photometric properties of raindrops, the algorithms rely on image processing techniques to highlight them. The results will be used to improve ADAS behaviour under rainy conditions. Both approaches are compared with each other and the techniques characterize rainy conditions. Both approaches are based on the photometric properties of the raindrops: brightness and blur. They rely on advanced image processing tools, such as morphological transformations, watershed and Background subtraction method is used to produce a mask localizing raindrops on the windshield. Finally, the rain detection is handled by counting the number of detected raindrops.

EXISTING METHOD

• Segmentation using Multilevel Thresholding
• Segmentation using Otsu technique.

DRAWBACKS

• This type of segmentation is not convenient for raindrop detection technique.
• Less accuracy.

PROPOSED METHOD

• Segmentation using Background Subtraction.
• Segmentation using watershed algorithm. 

BLOCK DIAGRAM

METHODOLOGIES

• Pre-processing
• Segmentation using (background subtraction, watershed algorithm)
• Analysis.

ADVANTAGES

• Accuracy was high

APPLICATIONS

• Automation applications

SOFTWARE REQUIREMENTS

• MATLAB 7 & Above versions

RESULTS

MATLAB GUI FOR DETECTING RAINDROPS

SELECTING AN INPUT VIDEO

FRAME SEPERATION

ANALYSIS

REFERENCES

[1] H. Kurihata, T. Takahashi, I. Ide, Y. Mekada, H. Murase, Y. Tamatsu, and T. Miyahara, “Rainy weather recognition from in-vehicle camera images for driver assistance,” in Proc. IEEE Intelligent Vehicles Symp., 2005, pp. 205–210.

[2] M. Roser and F. Moosmann, “Classification of weather situations on single color images,” in Proc. Intelligent Vehicles Symp., 2008, pp. 798–803.

[3] X. Yan, Y. Luo, and X. Zheng, “Weather recognition based on images captured by vision system in vehicle,” in Advances in Neural Networks (Lecture Notes in Computer Science, vol. 5553), W. Yu, H. He, and N. Zhang, Eds. Berlin Heidelberg, Germany: Springer-Verlag, 2009, pp. 390–398.

[4] S. Gormer, A. Kummert, S.-B. Park, and P. Egbert, “Vision-based rain sensing with an in-vehicle camera,” in Proc. IEEE Intelligent Vehicles Symp., 2009, pp. 279–284

[5] M. Roser and A. Geiger, “Video-based raindrop detection for improved image registration,” in Proc. IEEE Int. Conf. Computer Vision Workshops, 2009, pp. 570–577.