| dc.description.abstract | There are few kinds of automatic mobile platforms can move by following a person or a specific subject in various situations such as carrying commodity or people. These platforms can move by following the path of their guides, however, there might be some moving obstacles on their path. As a result, there should be some methods to detect whether there are real obstacles and prevent collisions. Our research is focused on detecting the obstacles in front of the autonomous vehicle and controlling the velocity of the vehicle in real-time by arranging a camera on the platform.
The method of obstacles detecting includes following steps. In the first step, the characteristic points are selected from the edge points the image for detecting the obstacles, in additional, split the image into a number of cells, then calculate HOG characteristics of each cell, and retain only the cell in response to an obviously direction information may be characterized as an obstacle edge to reduce interference noise. In the third step, a single image is separated in three different resolutions and the motion vectors are calculated more accurately and more efficiently by Lucas-Kanade method. In the fourth step, to make the following clustering be more accurate, the optical flow in the same plat are adjusted to be related with their positions. In the fifth step, different areas are clustered according to the length of the vectors and the color information. Moreover, the areas which may be plane subjects are removed and the regions which are overlapped are removed. As a result, the rest areas can be considered as the real obstacles in real world. Finally, the velocity of the autonomous vehicle is controlled according to the distance between the obstacles and the autonomous vehicle.
The real obstacle detection for autonomous vehicle is going to build on a electric wheelchair. The image detect device on the vehicle can be entered resolution of 240 x 320 images, in additional, the detect system is executed through a personal computer with Intel CoreTM i3-2370M 2.4GHz and 8GB RAM and the frame rate is 20 to 30 frames per second, as a result, the detection rate is about 90 %.
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