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• W2058700269 abstract "<abstract> <bold>Abstract.</bold> Obstacle recognition and motion state estimation are important elements in autonomous navigation systems. In this research, a stereo vision system consisting of dual cameras was designed and efficient algorithms for obstacle recognition and motions estimation of obstacles are proposed. To satisfy the geometric constraints of ideal stereo vision theory, the dual cameras are mounted on a specially designed mechanism which makes the optical axes of cameras parallel. When the distortion of images due to camera lens is corrected by calibration, the disparity image can be estimated by the correspondence matching method; thus the distance and three-dimensional information of obstacles can be obtained in real-time. To detect and locate obstacles, the estimated three-dimensional information of each pixel in image plane is projected onto a non-linear top-view map, and the blob segmentation is used to define the obstacle candidates in top-view map. Then the pre-defined three-dimensional constraints are imposed to filter noisy blobs and thus to accurately detect obstacles. Following the detection of obstacles, an obstacle recognition strategy is applied. The obstacles are first separated into elongated and non-elongated shape obstacles by the geometrical feature which is the ratio of height and width. The elongated shape obstacle candidates include pedestrians and unknown objects, and the non-elongated shape candidates consist of small vehicles, large vehicles, and unknown objects. To recognize the obstacle types, histogram of oriented gradient (HOG) is applied to extract features from the obstacle images. Before support vector machine (SVM) is employed, linear discriminant analysis (LDA) is used to reduce the complexity of HOG feature to simplify the feature dimension and speed up the recognition time. To track the obstacle appearing in different video frames, Bhattacharyya distance is used to be the matching index. When the obstacle is able to be tracked in different video frames, the motions of obstacles can also be estimated by using the Kalman filter. Moreover, the trajectories of obstacles are recorded. In our experiments, the developed stereo vision system operates at a speed of over 10 frames per second at a resolution of 640x480. The obstacle detection rate and the accuracy of obstacle recognition were all above 90% under various conditions. The error of motion estimation was about 50 cm." @default.
• W2058700269 created "2016-06-24" @default.
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• W2058700269 date "2013-01-01" @default.
• W2058700269 modified "2023-09-27" @default.
• W2058700269 title "A Real-Time Stereo Vision System for Obstacle Recognition and Motion Estimation" @default.
• W2058700269 doi "https://doi.org/10.13031/aim.20131597654" @default.
• W2058700269 hasPublicationYear "2013" @default.
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