UCF-CAP, VIDEO CAPTIONING IN THE WILD

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.