Abstract
An automatic surveillance system capable of detecting, classifying and localizing acoustic events in a bank operating room is presented. Algorithms for detection and classification of abnormal acoustic events, such as screams or gunshots are introduced. Two types of detectors are employed to detect impulsive sounds and vocal activity. A Support Vector Machine (SVM) classifier is used to discern between the different classes of acoustic events. The methods for calculating the direction of coming sound employing an acoustic vector sensor are presented. The localization is achieved by calculating the DOA (Direction of Arrival) histogram. The evaluation of the system based on experiments conducted in a real bank operating room is presented. The results of sound event detection, classification and localization are provided and discussed. The practical usability of the engineered methods is underlined by presenting the results of analyzing a staged robbery situation.
Highlights
Owing to the recent development of automatic visual and acoustic event detection methods, practical applications of audio-visual surveillance solutions are possible
The engineered algorithms were evaluated in practical conditions, in a real bank operating room, in the presence of typical background noise
It was shown that the automatic recognition of events enables a correct detection of both threatening and typical events
Summary
Owing to the recent development of automatic visual and acoustic event detection methods, practical applications of audio-visual surveillance solutions are possible. In our work we aim at detecting acoustic events in the bank environment, calculating the location of the sound sources and discerning between the threatening and non-threatening events. The knowledge of the location of the event (acoustic direction of arrival) can be used to improve the efficiency of security surveillance, e.g. by automatically pointing the PTZ camera towards the direction of the detected action. The methods employed for detection and classification, as well as the localization algorithm, are described . An attempt to assess the performance of the employed signal processing techniques in such difficult conditions is made.
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