Microphone array based classification for security monitoring in unstructured environments

Scardapane, Simone; Scarpiniti, Michele; Bucciarelli, Marta; Colone, Fabiola; Mansueto, Marcello Vincenzo; Parisi, Raffaele

doi:10.1016/j.aeue.2015.08.007

Cited by 15 publications

(11 citation statements)

References 30 publications

(36 reference statements)

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“…The traditional analysis method is manual real-time monitoring and video query and playback. With the explosive growth of video monitoring data, the current video monitoring system, data management mode, data analysis application and other technologies have not met the requirements due to the number and ability of personnel (prone to fatigue for a long time, wrong or missed views, too late to see), limited display equipment (the number of front-end cameras is far greater than the number of display screens) and other factors [31]- [35]. Specifically speaking, the current system architecture can not guarantee that all scenes can be monitored accurately and efficiently in 24 hours.…”

Section: Intelligent Video Retrieval and Its Application In Face mentioning

confidence: 99%

Face Detection in Security Monitoring Based on Artificial Intelligence Video Retrieval Technology

2020

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With the rapid development of video monitoring, the massive information of the monitoring image has far exceeded the effective processing range of human resources. Intelligent video retrieval technology has become an increasingly indispensable part of video monitoring system. Intelligent video retrieval technology integrates video processing, computer vision and artificial intelligence, which greatly improves the efficiency of monitoring and the accuracy and linkage of monitoring system. Face recognition and other emerging technologies continue to rise and apply to the security monitoring system. Based on deep learning theory and face detection neural network, this paper proposes a video oriented cascaded intelligent face detection algorithm, which builds deep learning network by cascading multiple features, from edge features, contour features, local features to semantic features, and advances layer by layer. According to the last semantic features, the information of the input data is obtained to accurately realize the face detection under the non ideal condition. Simulation results show that the algorithm has good detection performance for single face and multi face images, and has strong robustness for rotating face. At the same time, the algorithm is fast and can basically meet the requirements of real-time face detection. INDEX TERMS Artificial intelligence, video retrieval, security monitoring, face detection.

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Section: Intelligent Video Retrieval and Its Application In Face mentioning

confidence: 99%

Face Detection in Security Monitoring Based on Artificial Intelligence Video Retrieval Technology

2020

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“…In [12] and [9], the authors use beamforming to improve the classification of audios. The first one operates in the field of monitoring systems and, as in the current work, simulates an array of microphones and establishes the position of the sound source randomly.…”

Section: Literature Reviewmentioning

confidence: 99%

Microphone Array Based Surveillance Audio Classification

Silva¹,

Spadini²,

Suyama³

2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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The work assessed seven classifiers and two beamforming algorithms for detecting surveillance sound events. The tests included the use of AWGN with -10 dB to 30 dB SNR and Data Augmentation (DA). The results showed that the combination of Support Vector Machine (SVM) and Delay-and-Sum (DaS) scored the best accuracy (up to 86.0%), but had high computational cost (≈ 79 ms), mainly due to DaS and DA. The use of Stochastic Gradient Descent (SGD) also seems to be a good alternative since it has achieved good accuracy either (up to 85.3%), but with quicker processing time (≈ 25 ms).

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“…In last years, many research efforts have been made towards the event classification of audio data, due to the availability of cheap sensors [1]. In fact, systems based on acoustic sensors are of particular interest for their flexibility and cheapness [2].…”

Section: Introductionmentioning

confidence: 99%

A CNN Approach for Audio Classification in Construction Sites

Maccagno

Mastropietro

Mazziotta

et al. 2020

Smart Innovation, Systems and Technologies

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Convolutional Neural Networks (CNNs) have been widely used in the field of audio recognition and classification, since they often provide positive results. Motivated by the success of this kind of approach and the lack of practical methodologies for the monitoring of construction sites by using audio data, we developed an application for the classification of different types and brands of construction vehicles and tools, which operates on the emitted audio through a stack of convolutional layers. The proposed architecture works on the mel-spectrogram representation of the input audio frames and it demonstrates its effectiveness in environmental sound classification (ESC) achieving a high accuracy. In summary, our contribution shows that techniques employed for general ESC can be also successfully adapted to a more specific environmental sound classification task, such as event recognition in construction sites.

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Microphone array based classification for security monitoring in unstructured environments

Cited by 15 publications

References 30 publications

Face Detection in Security Monitoring Based on Artificial Intelligence Video Retrieval Technology

Face Detection in Security Monitoring Based on Artificial Intelligence Video Retrieval Technology

Microphone Array Based Surveillance Audio Classification

A CNN Approach for Audio Classification in Construction Sites

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