Using human pose information for handgun detection

Abstract

Fast automatic handgun detection can be very useful to avoid or mitigate risks in public spaces. Detectors based on deep learning methods have been proposed in the literature to trigger an alarm if a handgun is detected in the image. However, those detectors are solely based on the weapon appearance on the image. In this work, we propose to combine the detector with the individual’s pose information in order to improve overall performance. To this end, a model that integrates grayscale images from the output of the handgun detector and heatmap-like images that represent pose is proposed. The results show an improvement over the original handgun detector. The proposed network provides a maximum improvement of a 17.5% in AP of the proposed combinational model over the baseline handgun detector.

Vision and Crowdsensing Technology for an Optimal Response in Security: Project results

“…In that sense, we consider that extending the CCTV to avoid blind spots and modifying the infrastructure so that the images are not only used for a posteriori viewing but also for real-time analysis would allow a control of these events without the need to involve every single occupant of the building. Future work has been noted in [10]- [12] where pose and video analysis is used to improve the accuracy of detection of weapons and violent acts.…”

Firearm-related action recognition and object detection dataset for video surveillance systems

“…The timely identification of hazardous objects, such as firearms, within images is of utmost importance in mitigating potential harm [6] , [7] , [8] , [9] . The dataset presented herein offers a comprehensive assortment of authentic surveillance footage, thereby facilitating the advancement and evaluation of machine learning models within these domains.…”

A Systematic Review of Using Machine Learning and Natural Language Processing in Smart Policing

“…These efforts highlight the growing demand for automatic systems in policing, given the increasing rate of crime and the frequent use of handheld weapons like pistols and revolvers in illegal or criminal activities [77,[79][80][81][82][83][84][85][86]. Another study proposed a model to detect handguns based on the individual's pose, utilizing CNNs [87]. Using different architectures of CNN is a common practice for weapon detection in images, as it has shown exceptional performance in object recognition tasks [88].…”