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Despite the constant advances in computer vision, integrating modernsingle-image detectors in real-time handgun alarm systems in video-surveillanceis still debatable. Using such detectors still implies a high number of falsealarms and false negatives. In this context, most existent studies select oneof the latest single-image detectors and train it on a better dataset or usesome pre-processing, post-processing or data-fusion approach to further reducefalse alarms. However, none of these works tried to exploit the temporalinformation present in the videos to mitigate false detections. This paperpresents a new system, called MULTI Confirmation-level Alarm SysTem based onConvolutional Neural Networks (CNN) and Long Short Term Memory networks (LSTM)(MULTICAST), that leverages not only the spacial information but also thetemporal information existent in the videos for a more reliable handgundetection. MULTICAST consists of three stages, i) a handgun detection stage,ii) a CNN-based spacial confirmation stage and iii) LSTM-based temporalconfirmation stage. The temporal confirmation stage uses the positions of thedetected handgun in previous instants to predict its trajectory in the nextframe. Our experiments show that MULTICAST reduces by 80% the number of falsealarms with respect to Faster R-CNN based-single-image detector, which makes itmore useful in providing more effective and rapid security responses.

MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance