Energy-Efficient Acoustic Violence Detector for Smart Cities

Bautista-Durán, Marta; García-Gómez, Joaquín; Gil-Pita, Roberto; Mohíno-Herranz, Inma; Rosa-Zurera, Manuel

doi:10.2991/ijcis.10.1.89

Cited by 10 publications

(2 citation statements)

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“…Various approaches were proposed to overcome these problems. The authors of [6] reduce the processing demands by using only the audio data. Other methods, as the one proposed in [7], implies high bandwidth for video streams transfer to a powerful base station.…”

Section: Introductionmentioning

confidence: 99%

A Sensor Network Approach for Violence Detection in Smart Cities Using Deep Learning

Baba

Gui²,

Cernazanu

et al. 2019

Sensors

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Citizen safety in modern urban environments is an important aspect of life quality. Implementation of a smart city approach to video surveillance depends heavily on the capability of gathering and processing huge amounts of live urban data. Analyzing data from high bandwidth surveillance video streams provided by large size distributed sensor networks is particularly challenging. We propose here an efficient method for automatic violent behavior detection designed for video sensor networks. Known solutions to real-time violence detection are not suitable for implementation in a resource-constrained environment due to the high processing power requirements. Our algorithm achieves real-time processing on a Raspberry PI-embedded architecture. To ensure separation of temporal and spatial information processing we employ a computationally effective cascaded approach. It consists of a deep neural network followed by a time domain classifier. In contrast with current approaches, the deep neural network input is fed exclusively with motion vector features extracted directly from the MPEG encoded video stream. As proven by results, we achieve state-of-the-art performance, while running on a low computational resources embedded architecture.

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Section: Introductionmentioning

confidence: 99%

A Sensor Network Approach for Violence Detection in Smart Cities Using Deep Learning

Baba

Gui²,

Cernazanu

et al. 2019

Sensors

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“…To achieve less risk of premature stalling of the search, we used a method known as Elimination Tournament of GAs [78], that combines several small GAs in a tournament in which the original population of each GA is generated by a random crossover of the "winner" chromosomes from previous GAs.…”

mentioning

confidence: 99%

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study

Mohíno-Herranz

Gil-Pita

Rosa-Zurera

et al. 2019

Sensors

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Activity and emotion recognition based on physiological signal processing in health care applications is a relevant research field, with promising future and relevant applications, such as health at work or preventive care. This paper carries out a deep analysis of features proposed to extract information from the electrocardiogram, thoracic electrical bioimpedance, and electrodermal activity signals. The activities analyzed are: neutral, emotional, mental and physical. A total number of 533 features are tested for activity recognition, performing a comprehensive study taking into consideration the prediction accuracy, feature calculation, window length, and type of classifier. Feature selection to know the most relevant features from the complete set is implemented using a genetic algorithm, with a different number of features. This study has allowed us to determine the best number of features to obtain a good error probability avoiding over-fitting, and the best subset of features among those proposed in the literature. The lowest error probability that is obtained is 22.2%, with 40 features, a least squares error classifier, and 40 s window length.

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