Abstract
Road obstacle detection is an important component of the advanced driver assistance system, and to improve the speed and accuracy of road obstacle detection method is a vital task. In this article, fast image region-matching method based on the maximally stable extremal regions method is proposed to improve the speed of image matching. The theoretical feasibility of detection method combining monocular camera with inertial measurement unit (IMU) is clarified. The fast road obstacle detection method based on maximally stable extremal regions combining fast image region-matching method based on maximally stable extremal regions and the vision-IMU-based obstacle detection method is proposed to bypass obstacle classification and to reduce time and space complexity for road environment perception. The AdaBoost cascade detector, the speeded-up robust features-based obstacle detection method, and the proposed method are used to detect obstacles in outdoor contrast tests. Test results show that the proposed method has higher accuracy, and the reason of high accuracy is analyzed. The processing time of AdaBoost cascade detector, speeded-up robust features-based obstacle detection method, and proposed method are compared, and the results show that the proposed method has faster processing speed, and the reason of faster processing speed is analyzed.
Highlights
Road obstacle detection is an important component of the advanced driver assistance system and has attracted an extensive amount of interest from both academia and automobile industry
As mentioned in the third step of image region matching and obstacle detection in the “Process of fast road obstacle detection method based on maximally stable extremal regions (MSER)” subsection, t represents the moment of acquiring image data
Fast image region-matching method based on MSER is proposed
Summary
Road obstacle detection is an important component of the advanced driver assistance system and has attracted an extensive amount of interest from both academia and automobile industry. The fast image region-matching method based on MSER is used to simplify matching process and to improve matching speed, and the vision-IMU-based obstacle detection method is used to detect obstacles using less feature points. As mentioned in the third step of image region matching and obstacle detection in the “Process of fast road obstacle detection method based on MSER” subsection, t represents the moment of acquiring image data. The SURF-based detector is several times faster than SIFT and is more robust against different image transformations than SIFT.[37] In contrast tests, the SURF is used to detect feature points in motion compensation-based road obstacle detection method. PA: producer’s accuracy; UA: user’s accuracy; OA: overall accuracy; SURF: speeded-up robust features; MSER: maximally stable extremal regions
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