Contextualized Privacy Filters in Video Surveillance Using Crowd Density Maps

Abstract

The widespread growth in the adoption of digital video surveillance systems emphasizes the need for privacy preservation video analytics techniques. While these privacy aspects have shown big interest in recent years, little importance has been given to the concept of context-aware privacy protection filters. In this paper, we specifically focus on the dependency between privacy preservation and crowd density. We show that additional information about the crowd density in the scene can be used in order to adjust the level of privacy protection according to the local needs. This additional information cue consists of modeling time-varying dynamics of the crowd density using local features as an observation of a probabilistic crowd function. It also involves a feature tracking step which enables excluding feature points on the background. This process is favourable for the later density function estimation since the influence of features irrelevant to the underlying crowd density is removed. Then, the protection level of personal privacy in videos is adapted according to the crowd density. Afterwards, a framework for objective evaluation of the contextualized protection filters is proposed. The effectiveness of the proposed context-aware privacy filters has been demonstrated by assessing the intelligibility vs. privacy trade-off using videos from different crowd datasets.