For navigation tasks it is necessary to determine position of the ego vehicle relative to the road. One of the principal approaches is to detect road boundaries and lanes using a vision system in the vehicle. This paper presents a simple and robust method designed to detect and estimate the curvature of road lane boundaries from images provided by a monocular camera. First, we use Vector-lane-concept and Non-uniform B-Spline (NUBS) interpolation method to construct the boundaries road lane. Based on lane detection result, we estimate the curvature of left and right lane boundaries for Autonomous Guided Vehicle systems application. Some experimental results based on real world road images are presented. These simulation results show the efficiency, feasibility and robustness of the algorithm.
It is well known that most of the industrial robots have excellent repeatability in positioning. However, the absolute position errors of industrial robots are relatively poor, and in some cases the error may reach even several millimeters, which make it difficult to apply the robot system to vehicle assembly lines that need small position errors. In this paper, we have studied a method to reduce the absolute position error of robots using machine vision and neural network. The position/orientation of robot tool-end is compensated using a vision-based approach combined with a neural network, where a novel indirect calibration approach is presented in order to gather information for training the neural network. In the simulation, the proposed compensation algorithm was found to reduce the positional error to 98%. On average, the absolute position error was 0.029 mm. The application of the proposed algorithm in the actual robot experiment reduced the error to 50.3%, averaging 1.79 mm.
In dense traffic flow, car occlusion is usually one of the great challenges of vehicle detection and tracking in traffic monitoring systems. Current methods of car hypothesis such as symmetry or shadow based method work only with non-occluded cars. In this paper, we proposed an approach to car detection and counting using a new method of car hypothesis based on car windshield appearance which is the most feasible cue to hypothesize cars in occlusion situations. In hypothesis stage, Hough transformation is used to detect trapezoid-like regions where a car's windshield could be located, and then candidate car regions are estimated by the windshield region and its size. In verification stage, HOG descriptor and a well-collected dataset are used to train a linear SVM classifier for detecting cars at a high accuracy rate. Then, a tracking process based on Kalman filter is used to track the movement of detected cars in consecutive frames of traffic videos, followed by rule-based reasoning for counting decision. Experimental results on real traffic videos showed that the system is able to detect, track and count multiple cars including occlusion in dense traffic flow in real-time.
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