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.
|Title of host publication||17th Image Processing: Algorithms and Systems Conference, IPAS 2019|
|Number of pages||6|
|Publication status||Published - 13 Jan 2019|
|Event||17th Image Processing: Algorithms and Systems Conference, IPAS 2019 - Burlingame, United States|
Duration: 13 Jan 2019 → 17 Jan 2019
|Conference||17th Image Processing: Algorithms and Systems Conference, IPAS 2019|
|Period||13/01/19 → 17/01/19|