A Multi-Class Audio Classification Method With Respect To Violent Content In Movies Using Bayesian Networks

Γιαννακόπουλος, Θεόδωρος; Pikrakis, Aggelos; Theodoridis, Sergios

doi:10.1109/mmsp.2007.4412825

Cited by 53 publications

(33 citation statements)

References 5 publications

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“…They built concept detectors using a sound effects database, and tested them on a few movies. While their results on the sound effects database are good, comparable to the results reported by Giannakopoulos et al [1], the results on movies correspond to the results obtained by the ARF team in terms of performance, therefore corroborating the high variability of movies content. Previous works are all based on a fixed segmentation of the audio stream, using 0.5 to 1 second segments.…”

Section: Introductionsupporting

confidence: 85%

“…Number of codebooks and words per codebook: Although increasing the number of codebooks (N ∈ [1,3,9]) or the number of words extracted per codebooks (K ∈ [1,3]) increases the number of combinations and should have the same effect as increasing the size of the codebooks, we observe that recall tends to increase, and precision slightly dropps. We believe that using several words per codebook, i.e., K = 3, drastically improves the description capabilities of audio words.…”

Section: B Study On the Parametersmentioning

confidence: 86%

“…The histogram of frame energies is approximated using two Gaussians from which a threshold is obtained to find low-energy segments corresponding to silence 1 . The reason for silence segmentation is that gunshots and explosions are highly susceptible to be non silent.…”

Section: A Audio Stream Segmentationmentioning

confidence: 99%

“…On the one hand, Giannakopoulos et al [1] are interested in detecting events like music, speech, shots, fight sounds and screams. They obtain good results, but this is probably due to their cross validation technique.…”

Section: Introductionmentioning

confidence: 99%

“…This type of events has been shown to be of particular interest in the literature for various practical applications such as violence detection in movies for automatic ratings or parental control [1] or automatic alarms raising in surveillance videos [6].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Audio event detection in movies using multiple audio words and contextual Bayesian networks

Penet

Demarty

Gravier

et al. 2013

2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)

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Abstract-This article investigates a novel use of the wellknown audio words representations to detect specific audio events, namely gunshots and explosions, in order to get more robustness towards soundtrack variability in Hollywood movies. An audio stream is processed as a sequence of stationary segments. Each segment is described by one or several audio words obtained by applying product quantization to standard features. Such a representation using multiple audio words constructed via product quantisation is one of the novelties described in this work. Based on this representation, Bayesian networks are used to exploit the contextual information in order to detect audio events. Experiments are performed on a comprehensive set of 15 movies, made publicly available. Results are comparable to the state of the art results obtained on the same dataset but show increased robustness to decision thresholds, however limiting the range of possible operating points in some conditions. Late fusion provides a solution to this issue.

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Section: Introductionsupporting

confidence: 85%

Section: B Study On the Parametersmentioning

confidence: 86%

Section: A Audio Stream Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Audio event detection in movies using multiple audio words and contextual Bayesian networks

Penet

Demarty

Gravier

et al. 2013

2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)

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Feature Fusion Based Deep Spatiotemporal Model for Violence Detection in Videos

Asad

Yang

Khan

et al. 2019

Lecture Notes in Computer Science

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It is essential for public monitoring and security to detect violent behavior in surveillance videos. However, it requires constant human observation and attention, which is a challenging task. Autonomous detection of violent activities is essential for continuous, uninterrupted video surveillance systems. This paper proposed a novel method to detect violent activities in videos, using fused spatial feature maps, based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) units. The spatial features are extracted through CNN, and multi-level spatial features fusion method is used to combine the spatial features maps from two equally spaced sequential input video frames to incorporate motion characteristics. The additional residual layer blocks are used to further learn these fused spatial features to increase the classification accuracy of the network. The combined spatial features of input frames are then fed to LSTM units to learn the global temporal information. The output of this network classifies the violent or non-violent category present in the input video frame. Experimental results on three different standard benchmark datasets: Hockey Fight, Crowd Violence and BEHAVE show that the proposed algorithm provides better ability to recognize violent actions in different scenarios and results in improved performance compared to the state-of-the-art methods.

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Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision

Liu

Shi

et al. 2020

Lecture Notes in Computer Science

231

119

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A Multi-Class Audio Classification Method With Respect To Violent Content In Movies Using Bayesian Networks

Cited by 53 publications

References 5 publications

Audio event detection in movies using multiple audio words and contextual Bayesian networks

Audio event detection in movies using multiple audio words and contextual Bayesian networks

Feature Fusion Based Deep Spatiotemporal Model for Violence Detection in Videos

Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision

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