Hybrid system for automatic detection of gunshots in indoor environment
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Hybrid system for automatic detection of gunshots in indoor environment Sami Ur Rahman 1 & Adnan Khan 1 & Sohail Abbas 2 & Fakhre Alam 1
& Nasir Rashid
1
Received: 10 January 2020 / Revised: 12 August 2020 / Accepted: 17 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Automatic gunshot detection technology allows incidence response system to counteract the potential of crimes. However, the surveillance systems suffer from various detection problems, such as difficulty in differentiating gunshot, fire work and other similar sounds. To improves the accuracy and reduces processing time, we have proposed hybrid algorithm for automatic detection of gunshots in indoor environment. In the proposed approach, we have used pre-processing steps which filters the input audio signals with a threshold. During preprocessing, the signals having smaller energy than the threshold value are discarded because these low energy signals are normal sound signals. When energy of audio signal is more than the threshold value and deemed ambiguous audio, such signal is forwarded to next step for further processing. The second step of the proposed approach is based on features based algorithm, in which antilog energy features are implemented to increase accuracy. These features extend energy band to easily differentiate between gunshot and normal scream. For classification purpose, SVM, Tree and KNN classifiers are used comparatively to differentiate a classifier which will show more accuracy with minimal computational cost. The proposed approach provides 94.97% accuracy for SVM,92.56% accuracy for KNN classifier, and 91.65% accuracy for Tree classifier. The pre-processing step reduces computational time by 5%, 13.61% and 34.56% for KNN, Tree and SVM classifiers respectively. The preprocessing step in the proposed algorithm requires 5.80% processing time of features based approach to filter an audio signal. Keywords Gunshots . Muzzle Blast . Shook Wave . Audio Energy . Antilog Energy
1 Introduction Over the last two decades, terrorist attacks have been increased very rapidly. In these terrorist attacks, different places including civilian resorts, schools, colleges, universities and other
* Fakhre Alam [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
public and private locations have been targeted. Different security measures have been implemented to counter such security threats. These include deployment of security guards, security alarms installations, metal detectors, detection of explosive materials and availability of emergency call centers. However, due to prevalent complex natures of security threats, these traditional security measures do not work efficiently, and hence such measures could be easily counteracted. One of the solutions for dealing with robbery and terrorist attacks is the use of technology. Security cameras are deployed and the locations are monitored using CCTV (closed-circuit television)
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