Crowdsourcing Approach for Evaluation of Privacy Filters in Video Surveillance
Extensive adoption of video surveillance, affecting many aspects of the daily life, alarms the concerned public about the increasing invasion into personal privacy. To address these concerns, many tools have been proposed for protection of personal privacy in image and video. However, little is understood regarding the effectiveness of such tools and especially their impact on the underlying surveillance tasks. In this paper, we propose conducting a subjective evaluation using crowdsourcing to analyze the tradeoff between the preservation of privacy offered by these tools and the intelligibility of activities under video surveillance. As an example, the proposed method is used to compare several commonly employed privacy protection techniques, such as blurring, pixelization, and masking applied to indoor surveillance video. Facebook based crowdsourcing application was specifically developed to gather the subjective evaluation data. Based on more than one hundred participants, the evaluation results demonstrate that the pixelization filter provides the best performance in terms of balance between privacy protection and intelligibility. The results obtained with crowdsourcing application were compared with results of previous work using more conventional subjective tests showing that they are highly correlated.