Abstract
In this paper, we develop a vision based obstacle detection system by utilizing our proposed fisheye lens inverse perspective mapping (FLIPM) method. The new mapping equations are derived to transform the images captured by the fisheye lens camera into the undistorted remapped ones under practical circumstances. In the obstacle detection, we make use of the features of vertical edges on objects from remapped images to indicate the relative positions of obstacles. The static information of remapped images in the current frame is referred to determining the features of source images in the searching stage from either the profile or temporal IPM difference image. The profile image can be acquired by several processes such as sharpening, edge detection, morphological operation, and modified thinning algorithms on the remapped image. The temporal IPM difference image can be obtained by a spatial shift on the remapped image in the previous frame. Moreover, the polar histogram and its post-processing procedures will be used to indicate the position and length of feature vectors and to remove noises as well. Our obstacle detection can give drivers the warning signals within a limited distance from nearby vehicles while the detected obstacles are even with the quasi-vertical edges.
Highlights
With the fast growing number of vehicles and traffic accidents in recent years, the advanced vehicle control and safety driving assistance in intelligent transportation systems (ITS) have been more and more important
Based on the observation that those qualified features will always pass through the vertical projection point of cameras, we propose a feature searching algorithm and use polar histogram to accurately detect obstacles even for the noisy images
Our tracking process was carried out by iteratively checking the displacement and angular shift in the image, and we demonstrated the results of the tracking process in successive frames as given in Figures 23 and 24
Summary
With the fast growing number of vehicles and traffic accidents in recent years, the advanced vehicle control and safety driving assistance in intelligent transportation systems (ITS) have been more and more important. Nieto et al [8] introduced how to stabilize IPM images by using vanish point estimation In their approaches based on IPM, the planar objects such as lane markings were eliminated and the prominent objects like quasitriangle pairs were reserved. The performance of those detection methods would obviously depend on the height, width, distance, and shape of an obstacle. Kyo et al [13] used edges to detect possible vehicles and further validated the vehicles by the characteristics of symmetry, shadow, and differences in the gray-level average intensity, and Denasi and Quaglia [14] used pattern matching to detect and validate vehicles These methods would usually fail if the obstacles did not match the defined models. We have organized the following sections in this paper, including our systematic structure, the modified normal lens IPM method, fisheye lens IPM, obstacle detection algorithms, experimental results, and conclusions
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