Violence Detection in Video Using Spatio-Temporal Features

Souza, Fillipe D. M. de; Cámara-Chávez, Guillermo; Valle, Eduardo; Araújo, Arnaldo de Albuquerque

doi:10.1109/sibgrapi.2010.38

Cited by 109 publications

(45 citation statements)

References 12 publications

(18 reference statements)

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“…Frame-level audio features both from the time and the frequency domain are employed and a polynomial SVM is used as the classifier. In [8], de Souza et al adopt their own definition of violence, and designate violent scenes as those containing fights (i.e., aggressive human actions), regardless of the context and the number of people involved. Their SVM approach is based on the use of Bag-of-Words (BoW), where local Spatial-Temporal Interest Point Features (STIP) are used as feature representations.…”

Section: Related Workmentioning

confidence: 99%

“…In many of the existing works, next to audio or static visual cues, motion information is also used in the detection of violent scenes. Employed motion features range from simplistic features such as motion changes, shot length, camera motion or motion intensity [5,12,20,22], to more elaborated descriptors such as STIP, ViF [8,9] or dense trajectories, which have recently enjoyed great popularity. Dense trajectory features [25] have indeed received attention even among the VSD participants (e.g., [26,27,28]).…”

Section: Addressed Research Questions and Contributions Of This Papermentioning

confidence: 99%

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Breaking down violence detection: Combining divide-et-impera and coarse-to-fine strategies

2016

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Breaking down violence detection: combining divide-et-impera and coarse-to-fine strategies. Neurocomputing, 208, pp. 225-237. (doi:10.1016Neurocomputing, 208, pp. 225-237. (doi:10. /j.neucom.2016 This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/119160/

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Section: Related Workmentioning

confidence: 99%

Section: Addressed Research Questions and Contributions Of This Papermentioning

confidence: 99%

Breaking down violence detection: Combining divide-et-impera and coarse-to-fine strategies

2016

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“…In recent studies, some approaches based on spatiotemporal interest points (STIPs) [15] have been proposed for violence detection. Generally, after extracting interest points over the frames, the Bag-of-Words (BoW) approach is used for recognizing violence.…”

Section: Introductionmentioning

confidence: 99%

“…Approaches based on local spatiotemporal descriptors are traditionally combined with models of Bag-of-Words (BoWs) and have achieved promising performance in violence detection [2], [15]. However, the conventional BoW methods rely on the discriminative power of local spatiotemporal descriptors and how often these descriptors occur in the video.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Dictionary Learning With Motion Weber Local Descriptor for Violence Detection

Zhang

Jia

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in developing an algorithm that can detect violence in surveillance videos with high performance. In this paper, following our recently proposed idea of Motion Weber Local Descriptor (MoWLD), we make two major improvements and propose a more effective and efficient algorithm for detecting violence from motion images. First, we propose an improved WLD (IWLD) to better depict low-level image appearance information, and then extend the spatial descriptor IWLD by adding a temporal component to capture local motion information and hence form the MoIWLD. Second, we propose a modified sparse-representation-based classification (SRC) model to both control the reconstruction error of coding coefficients and minimize the classification error. Based on the proposed sparse model, a class-specific dictionary containing dictionary atoms corresponding to the class labels is learned by using class labels of training samples. With this learned dictionary, not only the representation residual but also the representation coefficients become discriminative. A classification scheme integrating the modified sparse model is developed to exploit such discriminative information. Experimental results on three benchmark datasets have demonstrated the superior performance of the proposed approach over the state-of-the-arts.

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“…This causes researchers difficulty in terms of working on a common ground [1]. Some of the violence interpretations include violent actions by humans where there is blood [2], scenes containing gunshots, fights and explosions [3], person to person harmful acts like threatening and physical harm [4], and fighting scenes regardless of number of individuals involved and context [5,6]. These different interpretations lead to different techniques for VSD, which makes it difficult to conduct a comparative study.…”

Section: Introductionmentioning

confidence: 99%

Violent Scene Detection Using a Super Descriptor Tensor Decomposition

Khokher

Bouzerdoum

Phung

2015

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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This article presents a new method for violent scene detection using super descriptor tensor decomposition. Multi-modal local features comprising auditory and visual features are extracted from Mel-frequency cepstral coefficients (including first and second order derivatives) and refined dense trajectories. There is usually a large number of dense trajectories extracted from a video sequence; some of these trajectories are unnecessary and can affect the accuracy. We propose to refine the dense trajectories by selecting only discriminative trajectories in the region of interest. Visual descriptors consisting of oriented gradient and motion boundary histograms are computed along the refined dense trajectories. In traditional bag-of-visual-words techniques, the feature descriptors are concatenated to form a single large feature vector for classification. This destroys the spatio-Temporal interactions among features extracted from multi-modal data. To address this problem, a super descriptor tensor decomposition is proposed. The extracted feature descriptors are first encoded using super descriptor vector method. Then the encoded features are arranged as tensors so as to retain the spatioTemporal structure of the features. To obtain a compact set of features for classification, the TUCKER-3 decomposition is applied to the super descriptor tensors, followed by feature selection using Fisher feature ranking. The obtained features are fed to a support vector machine classifier. Experimental evaluation is performed on violence detection benchmark dataset, MediaEval VSD2014. The proposed method outperforms most of the state-of-The-Art methods, achieving MAP2014 scores of 60.2% and 67.8% on two subsets of the dataset. Abstract-This article presents a new method for violent scene detection using super descriptor tensor decomposition. Multi-modal local features comprising auditory and visual features are extracted from Mel-frequency cepstral coefficients (including first and second order derivatives) and refined dense trajectories. There is usually a large number of dense trajectories extracted from a video sequence; some of these trajectories are unnecessary and can affect the accuracy. We propose to refine the dense trajectories by selecting only discriminative trajectories in the region of interest. Visual descriptors consisting of oriented gradient and motion boundary histograms are computed along the refined dense trajectories. In traditional bag-of-visual-words techniques, the feature descriptors are concatenated to form a single large feature vector for classification. This destroys the spatio-temporal interactions among features extracted from multi-modal data. To address this problem, a super descriptor tensor decomposition is proposed. The extracted feature descriptors are first encoded using super descriptor vector method. Then the encoded features are arranged as tensors so as to retain the spatio-temporal structure of the features. To obtain a compact set of features for classification, the TUCKER-3 de...

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Violence Detection in Video Using Spatio-Temporal Features

Cited by 109 publications

References 12 publications

Breaking down violence detection: Combining divide-et-impera and coarse-to-fine strategies

Breaking down violence detection: Combining divide-et-impera and coarse-to-fine strategies

Discriminative Dictionary Learning With Motion Weber Local Descriptor for Violence Detection

Violent Scene Detection Using a Super Descriptor Tensor Decomposition

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