  Current surveillance and control systems still require human supervision and
intervention. This work presents a novel automatic handgun detection system in
videos appropriate for both, surveillance and control purposes. We reformulate
this detection problem into the problem of minimizing false positives and solve
it by building the key training data-set guided by the results of a deep
Convolutional Neural Networks (CNN) classifier, then assessing the best
classification model under two approaches, the sliding window approach and
region proposal approach. The most promising results are obtained by Faster
R-CNN based model trained on our new database. The best detector show a high
potential even in low quality youtube videos and provides satisfactory results
as automatic alarm system. Among 30 scenes, it successfully activates the alarm
after five successive true positives in less than 0.2 seconds, in 27 scenes. We
also define a new metric, Alarm Activation per Interval (AApI), to assess the
performance of a detection model as an automatic detection system in videos.
