Object Detection under Illumination Variation with Privacy Protection

Abstract

〈Summary〉 We propose an object detection method that aims to protect privacy while providing video surveillance even when the illumination changes. Recently, many surveillance cameras have been installed in public spaces for monitoring activities. Unfortunately, there are too few authorized people to allow all of the resulting video streams to be perused continuously. Therefore, it is necessary to allow concerned citizens to watch the video streams. To permit this, we must address the privacy issue. In the proposed method, we first extract the object regions (which include private information) using the background estimated by taking account of illumination variations. Next, we filter the extracted regions to protect privacy. Experiments reveal that our method can successfully catch objects in surveillance videos, even when the objects stop for a long time under varying illumination conditions, while concealing the private information.