LiDAR-assisted Large-scale Privacy Protection in Street-view Cycloramas

Recently, privacy has a growing importance in several domains, especially in street-view images. The conventional way to achieve this is to automatically detect and blur sensitive information from these images. However, the processing cost of blurring increases with the ever-growing resolution of images. We propose a system that is cost-effective even after increasing the resolution by a factor of 2.5. The new system utilizes depth data obtained from LiDAR to significantly reduce the search space for detection, thereby reducing the processing cost. Besides this, we test several detectors after reducing the detection space and provide an alternative solution based on state-of-the-art deep learning detectors to the existing HoG-SVM-Deep system that is faster and has a higher performance.Comment: Accepted at Electronic Imaging 201

LiDAR assisted large-scale privacy protection in street view cycloramas

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

LiDAR-assisted large-scale privacy protection in street-view cycloramas

Recently, privacy has a growing importance in several domains, especially in street-view images. The conventional way to achieve this is to automatically detect and blur sensitive information from these images. However, the processing cost of blurring increases with the ever growing resolution of images. We propose a system that is cost-effective even after increasing the resolution by a factor of 2.5. The new system utilizes depth data obtained from LiDAR to significantly reduce the search space for detection, thereby reducing the processing cost. Besides this, we test several detectors after reducing the detection space and provide an alternative solution based on state-of-the-art deep learning detectors to the existing HoG-SVM-Deep system that is faster and has a higher performance

LiDAR-assisted large-scale privacy protection in street-view cycloramas

\u3cp\u3eRecently, privacy has a growing importance in several domains, especially in street-view images. The conventional way to achieve this is to automatically detect and blur sensitive information from these images. However, the processing cost of blurring increases with the ever growing resolution of images. We propose a system that is cost-effective even after increasing the resolution by a factor of 2.5. The new system utilizes depth data obtained from LiDAR to significantly reduce the search space for detection, thereby reducing the processing cost. Besides this, we test several detectors after reducing the detection space and provide an alternative solution based on state-of-the-art deep learning detectors to the existing HoG-SVM-Deep system that is faster and has a higher performance.\u3c/p\u3