| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 100 === As the map service becomes commonly used, map service providers provide more detailed information on map to satisfy the users’ needs. Street view service, as a brand new kind of map service, provides street level visual information. However, this service also causes the problem of privacy leakage. To protect individual’s and company’s privacy, we present a system that automatically detects vehicles and riders associated with their motorbikes/bicycles, and removes them as if they had never been there. Although street view service providers have made efforts on blurring human faces and license plates, we argue that the remaining features, such as license numbers and phone numbers printed on car bodies, and shapes of riders, could still leak privacy. Given a sequence of street view images that were consecutively captured by the camera car driving along a way, the developed system first determines the region of interest by filtering out noisy objects such as building and trees on the roadside. By conducting motion analysis between images, this system then determines candidate foreground seeds and background seeds, which are later fed to a GrabCut image segmentation module. This design avoids human input that is originally demanded in the conventional Grabcut approach. After removing detected vehicles, an exemplar-based inpainting method, with special designs on determination of filling priority and direction of texture propagation, is adopted to make pleasing reconstruction results.
In the experiments, we analyze properties of our datasets, evaluate performance of the vehicle detection process, and compare our inpainting method with others. The experimental results show that our system protects drivers’ privacy than the methods currently used by map service providers.
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