  In recent years we have seen an upsurge in terror attacks around the world.
Such attacks usually happen in public places with large crowds to cause the
most damage possible and get the most attention. Even though surveillance
cameras are assumed to be a powerful tool, their effect in preventing crime is
far from clear due to either limitation in the ability of humans to vigilantly
monitor video surveillance or for the simple reason that they are operating
passively. In this paper, we present a weapon detection system based on an
ensemble of semantic Convolutional Neural Networks that decomposes the problem
of detecting and locating a weapon into a set of smaller problems concerned
with the individual component parts of a weapon. This approach has
computational and practical advantages: a set of simpler neural networks
dedicated to specific tasks requires less computational resources and can be
trained in parallel; the overall output of the system given by the aggregation
of the outputs of individual networks can be tuned by a user to trade-off false
positives and false negatives; finally, according to ensemble theory, the
output of the overall system will be robust and reliable even in the presence
of weak individual models. We evaluated our system running simulations aimed at
assessing the accuracy of individual networks and the whole system. The results
on synthetic data and real-world data are promising, and they suggest that our
approach may have advantages compared to the monolithic approach based on a
single deep convolutional neural network.
