Violence Detection in Real Environments for Smart Cities

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

doi:10.1007/978-3-319-48799-1_52

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…12 The main characteristics of the used database are shown in Table 2. Related to the implemented validation, a tailored version of k-fold cross-validation has been used in the experiments to avoid loss of generalization of the results.…”

Section: Resultsmentioning

confidence: 99%

“…Perceptual analysis emulates human ear non-linear frequency response by creating a set of filters on non-linearly spaced frequency bands. 11 In the case of violence detection and considering a sampling frequency of 22,050 Hz, N = 25 cepstral coefficients are calculated, 12 so that there will be 25 different MFCCs per frame, denoted MFCC nm , n = 1,..., 25.…”

Section: Feature Extractionmentioning

confidence: 99%

“…They are explained in detail in the literature. 12 According to the previous parameter, the individuals will be ranked and only the best individuals survive and reproduce. The population is composed of 100 individuals, 10 of them will be chosen as parents, and they will generate the remaining 90 sons by crossover.…”

Section: Constrained Selection Of Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Efficient Acoustic Violence Detector for Smart Cities

Bautista-Durán¹,

García-Gómez²,

Gil-Pita³

et al. 2017

IJCIS

Self Cite

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Violence detection represents an important issue to take into account in the design of intelligent algorithms for smart environments. This paper proposes an energy-efficient system capable of acoustically detecting violence. In our solution, genetic algorithms are used to select the best subset of features with a constrained computational cost. Results demonstrate the viability of the system, thanks to the low cost that some violence features require, making feasible the implementation of the proposed method in a nowadays low power microprocessor.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Efficient Acoustic Violence Detector for Smart Cities

Bautista-Durán¹,

García-Gómez²,

Gil-Pita³

et al. 2017

IJCIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…The database utilized for design and testing aligns with the dataset employed in previous studies, specifically [27,28]. This dataset includes audio recordings from several drones, including models such as DJI Phantom 3, Cheerson CX 10, Parrot AR, Machine Racer, and Hobbyking FPV250, among others.…”

Section: Databasementioning

confidence: 99%

Analysis of Distance and Environmental Impact on UAV Acoustic Detection

Tejera-Berengue,

Zhu-Zhou,

Utrilla-Manso

et al. 2024

Electronics

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This article explores the challenge of acoustic drone detection in real-world scenarios, with an emphasis on the impact of distance, to see how sound propagation affects drone detection. Learning machines of varying complexity are used for detection, ranging from simpler methods such as linear discriminant, multilayer perceptron, support vector machines, and random forest to more complex approaches based on deep neural networks like YAMNet. Our evaluation meticulously assesses the performance of these methods using a carefully curated database of a wide variety of drones and interference sounds. This database, processed through array signal processing and influenced by ambient noise, provides a realistic basis for our analyses. For this purpose, two different training strategies are explored. In the first approach, the learning machines are trained with unattenuated signals, aiming to preserve the inherent information of the sound sources. Subsequently, testing is then carried out under attenuated conditions at various distances, with interfering sounds. In this scenario, effective detection is achieved up to 200 m, which is particularly notable with the linear discriminant method. The second strategy involves training and testing with attenuated signals to consider different distances from the source. This strategy significantly extends the effective detection ranges, reaching up to 300 m for most methods and up to 500 m for the YAMNet-based detector. Additionally, this approach raises the possibility of having specialized detectors for specific distance ranges, significantly expanding the range of effective drone detection. Our study highlights the potential of drone acoustic detection at different distances and encourages further exploration in this research area. Unique contributions include the discovery that training with attenuated signals with a worse signal-to-noise ratio allows the improvement of the general performance of learning machine-based detectors, increasing the effective detection range achieved, and the feasibility of real-time detection, even with very complex learning machines, opening avenues for practical applications in real-world surveillance scenarios.

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“…Sharma et al realized violent video scene detection based on a deep neural network [17]. In terms of auditory, Garcia-Gomez et al realized the recognition of violent events based on auditory feature screening [18], while Chen L B et al explored the classification of violent audio using a temporal tensor feature [19]. Moreover, Wang Y et al examined audio time localization based on a continuous time model [20].…”

Section: Introductionmentioning

confidence: 99%

Violence Recognition Based on Auditory-Visual Fusion of Autoencoder Mapping

Lou¹,

Zuo²,

Zhang³

et al. 2021

Electronics

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In the process of violence recognition, accuracy is reduced due to problems related to time axis misalignment and the semantic deviation of multimedia visual auditory information. Therefore, this paper proposes a method for auditory-visual information fusion based on autoencoder mapping. First, a feature extraction model based on the CNN-LSTM framework is established, and multimedia segments are used as whole input to solve the problem of time axis misalignment of visual and auditory information. Then, a shared semantic subspace is constructed based on an autoencoder mapping model and is optimized by semantic correspondence, which solves the problem of audiovisual semantic deviation and realizes the fusion of visual and auditory information on segment level features. Finally, the whole network is used to identify violence. The experimental results show that the method can make good use of the complementarity between modes. Compared with single-mode information, the multimodal method can achieve better results.

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Violence Detection in Real Environments for Smart Cities

Cited by 8 publications

References 7 publications

Energy-Efficient Acoustic Violence Detector for Smart Cities

Energy-Efficient Acoustic Violence Detector for Smart Cities

Analysis of Distance and Environmental Impact on UAV Acoustic Detection

Violence Recognition Based on Auditory-Visual Fusion of Autoencoder Mapping

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