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.
Highlights
The rapid detection of handguns in public spaces is essential to avoid or mitigate risks [1]
The proposed network provides a maximum improvement of a 17.5% in average precision (AP) of the proposed combinational model over the baseline handgun detector
The IoMin threshold to classify a detection as true or false positive has been set to a 50% of overlap for all measurements
Summary
The rapid detection of handguns in public spaces is essential to avoid or mitigate risks [1]. Surveillance by means of closed-circuit television (CCTV) has been widely used to detect those situations, it requires continuous supervision of the images This task is usually handled by a human operator, which is likely to miss them due to fatigue or visual distraction. Deep learning techniques have proven to be specially powerful at automating this kind of visual tasks, where novel methods such as convolutional neural networks (CNNs) achieve good results in object detection. These methods are based on the generalization from a set of training samples, which may differ from the actual scenarios where they are used.
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