Abstract
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.
Highlights
Video surveillance has come a long way in the past decades
Object detection models are evaluated using Precision, Recall, and Average Precision (PASCAL VOC AP50) metrics [35]. These metrics are based on True Positives (TP), False Positives (FP) and False Negatives (FN)
In the same way as in the automatic labeling process for the training of the hand region classifier (Subsection 4.3), the intersection over minimum area (IoMin) is the selected criterion to calculate the overlap between the predicted bounding boxes and the ground truth data, due to the size difference between them
Summary
Public or private spaces such as train stations, airports, museums, banks or government institutional buildings have their own video surveillance systems. These systems are very useful for post-event investigations and assisting the security personnel to manage crowds, being able to monitor different locations simultaneously. The increasing number of areas controlled by video surveillance cameras, as well as factors inherent to human. Related studies in this area show that early detection of security threats or risks is fundamental to mitigate the damage caused as much as possible [3]. Situations involving firearms such as handgun attacks, mass shootings, gunfire incidents on school grounds [4] or terrorist attacks [5] are representative examples of this kind of threats, which have become rather common nowadays
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