Recent technological advances in the fields of data and com- puter science have improved significantly the everyday life of people. However, technological advances are also being adopted by criminals to facilitate and expand their illicit ac- tions. The Deep Learning (DL) paradigm has shown a signifi- cant potential in analysing complex structured data. However, in the crime detection domain, a limited number of public datasets is available, constrained to specific tasks only, which hinders the research and development of accurate and robust DL-assisted tools. The goal of this work is to extend the well- known UCF-crime dataset to the case of video captioning. To the best of our knowledge, this is the first publicly avail- able crime-related video captioning dataset. A new proposed video captioning approach is compared to a plethora of state- of- the-art-methods in this dataset, while qualitative and quan- titative characteristics of the latter are presented.