An increase in adoption of video surveillance, affecting many aspects of daily lives, raises public concern about an intrusion into individual privacy. New sensing and surveillance tech-nologies, such as mini-drones, threaten to eradicate bound-aries of private space even more. Therefore, it is important
to study the effect of mini-drones on privacy intrusion and to understand how existing protection privacy filters per-form on a video captured by a mini-drone. To this end, we have built a publicly available video dataset of typical drone-based surveillance sequences in a car parking. Using the sequences from this dataset, we assessed five privacy pro-tection filters at different strength levels via a crowdsourcing evaluation. We asked crowdsourcing workers several privacy- and surveillance-related questions to determine the tradeoff between intelligibility of the scene and privacy protection provided by the filters.
