Handgun detection using combined human pose and weapon appearance

TL;DR

This paper introduces a novel deep learning method that combines human pose and weapon appearance cues to improve handgun detection accuracy in CCTV footage, outperforming previous approaches.

Contribution

The work presents a new architecture integrating pose keypoints and appearance features, enhancing detection performance over existing methods.

Findings

Achieved 4.23 to 18.9 AP points improvement over previous state-of-the-art.

Demonstrated the effectiveness of combining pose and appearance cues.

Enhanced handgun detection accuracy in CCTV scenarios.

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.