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.
Original language | English |
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Title of host publication | IS&T International Symposium on Electronic Imaging |
Publication status | Published - 2018 |
Event | IS&T International Symposium on Electronic Imaging Science and Technology, : Image Processing: Algorithms and Systems XV - Burlingame, United States Duration: 29 Jan 2017 → 2 Feb 2017 |
Conference
Conference | IS&T International Symposium on Electronic Imaging Science and Technology, |
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Country/Territory | United States |
City | Burlingame |
Period | 29/01/17 → 2/02/17 |