In this paper, we propose a new comprehensive Video Surveillance Quality Assessment Dataset (VSQuAD) dedicated to Video Surveillance (VS) systems. In contrast to other public datasets, this one contains many more videos with distortions and diversified content from common video surveillance scenarios. These videos have been artificially degraded with various types of distortions (single distortion or multiple distortions simultaneously) at different severity levels. In order to improve the efficiency of the surveillance systems and the versatility of the video quality assessment dataset, night vision CCTV videos are also included. Furthermore, a comprehensive analysis of the content in terms of diversity and challenging problems is also presented in this study. The interest of such database is twofold. First, it will serve for benchmarking different video distortion detection and classification algorithms. Second, it will be useful for the design of learning models for various challenging VS problems such as identification and removal of the most common distortions. The complete dataset is made publicly available as part of a challenge session in this conference through the following link: https://www.l2ti.univ-paris13.fr/VSQuad/.