Intended human object detection for automatically protecting privacy in mobile video surveillance

Abstract

With the recent popularization of mobile video cameras including camera phones, a new technology, mobile video surveillance, which uses mobile video cameras for video surveillance has been emerging. Such videos, however, may infringe upon the privacy of others by disclosing privacy sensitive information (PSI), i.e., their appearances. To prevent videos from infringing on the right to privacy, new techniques are required that automatically obscure PSI regions. The problem is how to determine the PSI regions to be obscured while maintaining enough video content to present the camera persons’ capture-intentions, i.e., what they want to record in their videos to achieve their surveillance tasks. To this end, we introduce a new concept called intended human objects that are defined as human objects essential for capture-intentions, and develop a new method called intended human object detection that automatically detects the intended human objects in videos taken by different camera persons. Through the process of intended human object detection, we develop a system for automatically obscuring PSI regions. We experimentally show the performance of intended human object detection and the contributions of the features used. Our user study shows the potential applicability of our proposed system.