  Despite the constant advances in computer vision, integrating modern
single-image detectors in real-time handgun alarm systems in video-surveillance
is still debatable. Using such detectors still implies a high number of false
alarms and false negatives. In this context, most existent studies select one
of the latest single-image detectors and train it on a better dataset or use
some pre-processing, post-processing or data-fusion approach to further reduce
false alarms. However, none of these works tried to exploit the temporal
information present in the videos to mitigate false detections. This paper
presents a new system, called MULTI Confirmation-level Alarm SysTem based on
Convolutional Neural Networks (CNN) and Long Short Term Memory networks (LSTM)
(MULTICAST), that leverages not only the spacial information but also the
temporal information existent in the videos for a more reliable handgun
detection. MULTICAST consists of three stages, i) a handgun detection stage,
ii) a CNN-based spacial confirmation stage and iii) LSTM-based temporal
confirmation stage. The temporal confirmation stage uses the positions of the
detected handgun in previous instants to predict its trajectory in the next
frame. Our experiments show that MULTICAST reduces by 80% the number of false
alarms with respect to Faster R-CNN based-single-image detector, which makes it
more useful in providing more effective and rapid security responses.
