The paper presents an algorithm for road markings detection in the image. The road markings are approximated by polyline with a restricted maximum curvature angle. To detect a marking segments an image is processed by a sliding window and for each window position, a straight line is detected by calculating Fast Hough Transform (FHT). Further, detected segments are grouped by relative position. Segments groups are then approximated by polylines. The algorithm was tested on real data collected from the front-looking camera of the autonomous vehicle driving at the experimental area “Kalibr” (Moscow). The road marking dataset used to evaluate the algorithm is publicly available at ftp://vis.iitp.ru/road markup dataset/. The precision of road markings detector was evaluated as 43%, and the recall as 73% which is sufficient for the autonomous vehicle precise positioning as demonstrated in [1].
The growing popularity of mobile services increases the risks of financial and other losses from fraudulent user actions. To reduce the number of illegal actions and comply with the law when using mobile services, it is often scheme that user presents it’s identity document. In case of remote access via a mobile device, this means receiving and analyzing a video of a document image. One of the criteria for the authenticity of the captured security document is the presence of security optically variable devices (kinegrams, holograms). Reliable determination of the presence or absence of such security elements from video stream frames is greatly complicated by changes in color between frames. The paper discusses the possibility of using a priori information about the monochrome photograph of the document owner to compensate changes in color between frames. Color distributions are investigated on the example of black-and-white photographs. A new method for automatic white balance correction is proposed. The results of the method are tested on real data obtained with a mobile device.
In this paper we consider a method for detecting end-to-end curves of limited curvature like the k-link polylines with bending angle between adjacent segments in a given range. The approximation accuracy is achieved by maximization of the quality function in the image matrix. The method is based on a dynamic programming scheme constructed over Fast Hough Transform calculation results for image bands. The proposed method asymptotic complexity is O(h⋅(w+h/k)⋅log(h/k)), where h and w are the image size, and k is the approximating polyline links number, which is an analogue of the complexity of the fast Fourier transform or the fast Hough transform. We also show the results of the proposed method on synthetic and real data.
In this paper, we present the precise indoor positioning system for mobile robot pose estimation based on visual edge detection. The set of onboard motion sensors (i.e. wheel speed sensor and yaw rate sensor) is used for pose prediction. A schematic plan of the building, stored as a multichannel raster image, is used as a prior information. The pose likelihood estimation is performed via matching of edges, detected on the optical image, against the map. Therefore, the proposed method does not require any deliberate building infrastructure changes and makes use of the inherent features of manmade structures - edges between walls and floor. The particle filter algorithm is applied in order to integrate heterogeneous localization data (i.e. motion sensors and detected visual features). Since particle filter uses probabilistic sensor models for state estimation, the precise measurement noise modeling is key to positioning quality enhancement. The probabilistic noise model of the edge detector, combining geometrical detection noise and false positive edge detection noise, is proposed in this work. Developed localization system was experimentally evaluated on the car-like mobile robot in the challenging environment. Experimental results demonstrate that the proposed localization system is able to estimate the robot pose with a mean error not exceeding 0.1 m on each of 100 test runs.
The proposed algorithm matches local coordinates of each image with coordinates of the map by extracting roads’ line segments from the image and finding geometric transformation successfully matching the roads’ segments and the map’s ones. Parameters estimation is based on RANSAC algorithm which analyses the segments’ location and orientation.
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