Audio-Visual Surveillance System for Application in Bank Operating Room

Kotus, Józef; Lopatka, Kuba; Czyżewski, Andrzej; Bogdanis, Georgis

doi:10.1007/978-3-642-38559-9_10

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The authors' experiences with the sound source localization based on sound intensity methods performed in the time domain or in the frequency domain were presented in our previous papers [16][17][18]. The developed algorithm used for acoustic events localization operates in the frequency domain.…”

Section: Acoustic Events Localizationmentioning

confidence: 99%

Processing of acoustical data in a multimodal bank operating room surveillance system

Kotus

źOpatka

Czyżewski

et al. 2014

Multimed Tools Appl

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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.

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Section: Acoustic Events Localizationmentioning

confidence: 99%

Processing of acoustical data in a multimodal bank operating room surveillance system

Kotus

źOpatka

Czyżewski

et al. 2014

Multimed Tools Appl

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“…Video surveillance systems have been around for many years but have limitations such as limited field of view and relatively expensive computation and data storage. An ASR system could be used to complement a video-based recognition system such as in public transports [139], detecting traffic events [140], fall detection [141], machine awareness [142], and in banks [143]. Audio and video recognition systems could also be combined for recognition of complex events in real movies [144].…”

Section: Spectral Features Hmmmentioning

confidence: 99%

An overview of applications and advancements in automatic sound recognition

Sharan

Moir

2016

Neurocomputing

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a b s t r a c tAutomatic sound recognition (ASR) has attracted increased and wide ranging interests in recent years. In this paper, we carry out a review of some important contributions in ASR techniques, mainly over the last one and a half decades. Similar to speech recognition systems, the robustness of an ASR system largely depends on the choice of feature(s) and classifier(s). We take a wider perspective in providing an overview of the features and classifiers used in ASR systems starting from early works in content-based audio classification to more recent developments in applications such as sound event recognition, audio surveillance, and environmental sound recognition. We also review techniques that have been utilized in noise robust sound recognition systems and feature optimization methods. Finally, some of the less commonly known applications of ASR are discussed.

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Audio Surveillance

et al. 2016

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Despite surveillance systems becoming increasingly ubiquitous in our living environment, automated surveillance, currently based on video sensory modality and machine intelligence, lacks most of the time the robustness and reliability required in several real applications. To tackle this issue, audio sensory devices have been incorporated, both alone or in combination with video, giving birth in the past decade, to a considerable amount of research. In this article, audio-based automated surveillance methods are organized into a comprehensive survey: A general taxonomy, inspired by the more widespread video surveillance field, is proposed to systematically describe the methods covering background subtraction, event classification, object tracking, and situation analysis. For each of these tasks, all the significant works are reviewed, detailing their pros and cons and the context for which they have been proposed. Moreover, a specific section is devoted to audio features, discussing their expressiveness and their employment in the above-described tasks. Differing from other surveys on audio processing and analysis, the present one is specifically targeted to automated surveillance, highlighting the target applications of each described method and providing the reader with a systematic and schematic view useful for retrieving the most suited algorithms for each specific requirement.

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Audio-Visual Surveillance System for Application in Bank Operating Room

Cited by 3 publications

References 11 publications

Processing of acoustical data in a multimodal bank operating room surveillance system

Processing of acoustical data in a multimodal bank operating room surveillance system

An overview of applications and advancements in automatic sound recognition

Audio Surveillance

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