Violent Video Event Detection Based on Integrated LBP and GLCM Texture Features

Lohithashva, B. H.; Aradhya, V. N. Manjunath; Guru, D. S.

doi:10.18280/ria.340208

Cited by 29 publications

(16 citation statements)

References 30 publications

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“…In order to make full use of the texture information in the gray image, extracting the GLCM and Gabor features of the fiber to construct the texture feature vector. [18][19][20][21] ]. Because the contrast and angle second moment, which respectively represent the response to the clarity of the fiber image and the measure of gray distribution and fineness of the texture, are used as descriptors of fiber image in both spatial and frequency domain.…”

Section: Selection Of Parameters Based Onmentioning

confidence: 99%

Identification method of cashmere and wool based on texture features of GLCM and Gabor

Zhu

Huang

et al. 2021

Journal of Engineered Fibers and Fabrics

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The common texture feature extraction method is only in spatial or frequency domain, leading to insufficient texture information and low accuracy. The main aim of this paper is to present a novel texture feature analysis method based on gray level co-occurrence matrix and Gabor wavelet transform to sufficiently extract texture feature of cashmere and wool fibers. Firstly, the gray level co-occurrence matrix is constructed to calculate the four texture feature vectors including of contrast, angular second moment, dissimilarity and energy in spatial domain, and four texture feature vectors, which are contrast, angular second moment, mean and entropy, in frequency domain is obtained through Gabor wavelet transform and Gray-Scale difference statistics method. Then, because the contrast and angle second moment are used as descriptors of fiber image in both spatial and frequency domain, they are fused respectively by introducing a weight to make linear addition, making eight feature values compose a 6-dimensional feature vector. Finally, these feature vectors are fed into the Fisher classifier. The experimental results show that the identification accuracy of the proposed algorithm is improved by 0.682% compared to use 8-dimensional feature vectors describing the sample image. It verifies that the fused method based on texture feature in spatial and frequency domain is an effective approach to identify fibers of cashmere and wool.

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Section: Selection Of Parameters Based Onmentioning

confidence: 99%

Identification method of cashmere and wool based on texture features of GLCM and Gabor

Zhu

Huang

et al. 2021

Journal of Engineered Fibers and Fabrics

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show abstract

“…For time utilization, they found normal and abnormal features from videos using a deep learning-based approach. In [21], Lohithashva et al developed a novel fusion features descriptors method for violent event detection using local binary pattern (LBP) and gray level co-occurrence matrix (GLCM). They used supervised classification algorithms with extracted features for event classification.…”

Section: Sustainable Event Classification Via 2d/3d Imagesmentioning

confidence: 99%

Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing

2020

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This paper suggests that human pose estimation (HPE) and sustainable event classification (SEC) require an advanced human skeleton and context-aware features extraction approach along with machine learning classification methods to recognize daily events precisely. Over the last few decades, researchers have found new mechanisms to make HPE and SEC applicable in daily human life-log events such as sports, surveillance systems, human monitoring systems, and in the education sector. In this research article, we propose a novel HPE and SEC system for which we designed a pseudo-2D stick model. To extract full-body human silhouette features, we proposed various features such as energy, sine, distinct body parts movements, and a 3D Cartesian view of smoothing gradients features. Features extracted to represent human key posture points include rich 2D appearance, angular point, and multi-point autocorrelation. After the extraction of key points, we applied a hierarchical classification and optimization model via ray optimization and a K-ary tree hashing algorithm over a UCF50 dataset, an hmdb51 dataset, and an Olympic sports dataset. Human body key points detection accuracy for the UCF50 dataset was 80.9%, for the hmdb51 dataset it was 82.1%, and for the Olympic sports dataset it was 81.7%. Event classification for the UCF50 dataset was 90.48%, for the hmdb51 dataset it was 89.21%, and for the Olympic sports dataset it was 90.83%. These results indicate better performance for our approach compared to other state-of-the-art methods.

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“…Evaluating their approach with a privately held dataset and three public datasets, they reported a ROC score of 0.9782, 0.9403, 0.8218 and 0.9956. Recently, one more texture feature descriptor approach is suggested by Lohithashva et al [76] and based on LBP (Local Binary Pattern) and GLCM (Gray Level Co-occurrence Matrix). Prominent features were used with five different supervised classifiers on two standard benchmark datasets.…”

Section: Approaches Using Dynamic Texturesmentioning

confidence: 99%

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

Lejmi¹,

Khalifa²,

Mahjoub³

2020

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In the current era, the implementation of automated security video surveillance systems is particularly needy in terms of human violence recognition. Nevertheless, the latter encounters various interlinked difficulties which require efficient solutions as well as feasible methods that provide a relevant distinction between normal human actions and abnormal ones. In this paper, we present an overview of these issues and a literature review of the related works and current research on-going efforts on this field and suggests a novel prediction model for violence recognition, based on a preliminary spatio-temporal features extraction using the material derivative which describes the rate of change of a particle while in motion with respect to time. The classification algorithm is then carried out using a deep learning LSTM technique to classify generated features into eight specified violent and non-violent categories and a prediction value for each class of action is calculated. The whole model is trained on a public dataset and its classification capacity is evaluated on a confusion matrix which assembles all the predictions made by the system with their actual labels. The obtained results are promising and show that the proposed model can be potentially useful for detecting human violence.

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Violent Video Event Detection Based on Integrated LBP and GLCM Texture Features

Cited by 29 publications

References 30 publications

Identification method of cashmere and wool based on texture features of GLCM and Gabor

Identification method of cashmere and wool based on texture features of GLCM and Gabor

Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

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