  Threat detection of weapons and aggressive behavior from live video can be
used for rapid detection and prevention of potentially deadly incidents such as
terrorism, general criminal offences, or even domestic violence. One way for
achieving this is through the use of artificial intelligence and, in
particular, machine learning for image analysis. In this paper we conduct a
comparison between a traditional monolithic end-to-end deep learning model and
a previously proposed model based on an ensemble of simpler neural networks
detecting fire-weapons via semantic segmentation. We evaluated both models from
different points of view, including accuracy, computational and data
complexity, flexibility and reliability. Our results show that a semantic
segmentation model provides considerable amount of flexibility and resilience
in the low data environment compared to classical deep model models, although
its configuration and tuning presents a challenge in achieving the same levels
of accuracy as an end-to-end model.
