Abstract:
Video analytics is an integral part of surveillance cameras. Compared to video analytics, audio analytics offers several benefits, including
less expensive equipment and upkeep expenses. Additionally, the volume of
the audio datastream is substantially lower than the video camera datastream, especially concerning real-time operating systems, which makes it less
demanding of the data channel’s bandwidth needs. For instance, automatic
live video streaming from the site of an explosion and gunshot to the police
console using audio analytics technologies would be exceedingly helpful for
urban surveillance. Technologies for audio analytics may also be used to
analyze video recordings and identify occurrences. This research proposed
a deep learning model based on the combination of convolutional neural
network (CNN) and recurrent neural network (RNN) known as the CNNRNN approach. The proposed model focused on automatically identifying
pulse sounds that indicate critical situations in audio sources. The algorithm’s
accuracy ranged from 95% to 81% when classifying noises from incidents,
including gunshots, explosions, shattered glass, sirens, cries, and dog barking.
The proposed approach can be applied to provide security for citizens in open
and closed locations, like stadiums, underground areas, shopping malls, and
other places.
