Emerging new imaging technologies, such as HDR (High Dynamic Range) or UHD (Ultra High Definition), have a significant impact in various applications. One of them, namely, visual privacy protection in video surveillance is an important and increasingly popular research topic. To evaluate the influence of new imaging technologies, a suitable dataset of images and video sequences is needed. However, there are only few datasets for testing performance of various algorithms on above mentioned applications, and moreover, a little to nothing exists for evaluation of visual privacy tools. Since surveillance and privacy protection have contradictory objectives, the design principles of corresponding evaluation datasets should differ too. In this paper, we present a new dataset of still and moving pictures considering the predefined principles for privacy evaluation in video surveillance applications. Annotations of various privacy related regions, such as face, hair, body silhouette, skin regions, accessories, and other personal information, including their description, are provided within the dataset. Presented dataset will serve for evaluation of privacy protection tools using both objective and subjective assessment, as well as for showing the importance of HDR imaging in video surveillance applications and its influence on the privacy-intelligibility trade-off.