  Closed-circuit television (CCTV) systems are essential nowadays to prevent
security threats or dangerous situations, in which early detection is crucial.
Novel deep learning-based methods have allowed to develop automatic weapon
detectors with promising results. However, these approaches are mainly based on
visual weapon appearance only. For handguns, body pose may be a useful cue,
especially in cases where the gun is barely visible. In this work, a novel
method is proposed to combine, in a single architecture, both weapon appearance
and human pose information. First, pose keypoints are estimated to extract hand
regions and generate binary pose images, which are the model inputs. Then, each
input is processed in different subnetworks and combined to produce the handgun
bounding box. Results obtained show that the combined model improves the
handgun detection state of the art, achieving from 4.23 to 18.9 AP points more
than the best previous approach.
