We introduce a new dataset, dubbed UNICITY , for the task of detecting people in security airlocks in top view depth images. If security companies have been relying on computer systems and algorithms for a long time, very few are trusting artificial intelligence and more specifically machine learning approaches in production environments. We are confident that the recent advances in these domains, especially with the democratization of deep learning, will open new horizons for security systems. We release this dataset to encourage the development of such approaches in the scientific community. UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries (e.g. tailgating). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications. This paper presents the data collection, an evaluation protocol, as well as two baseline methods for attack detection.