Abstract
The past decade has seen a rise in capturing public spaces for providing well-organized and geo-positioned databases of street view imagery. However, capturing public spaces is challenging, as they contain privacy-sensitive objects, such as faces and license plates. Therefore, these objects must be detected and blurred through an automated process. Although automated methods are labour-free, large resolution images incur high costs for processing. In this research, as we transition from 100 to 250-megapixel system (per cyclorama), we present a framework that reduces the search space of a detection algorithm using depth data obtained from a LIDAR scanner. After then increasing the resolution by 2.5 times and comparing several deep learning architectures, we manage to keep execution time at nearly the same time.
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