Violence detection in videos for an intelligent surveillance system using MoBSIFT and movement filtering algorithm

Febin, I. P.; Jayasree, K.; Joy, Preetha Theresa

doi:10.1007/s10044-019-00821-3

Cited by 65 publications

(26 citation statements)

References 28 publications

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“…They got the background cues from spatio-temporal characteristics which have been filtered by the homography transformation. To check videos, Febin et al [25] filtered the movement by means of temporal derivative and did not extract features from most of the nonviolent activities.…”

Section: Camera Motionmentioning

confidence: 99%

“…The obtained results are very encouraging: 94.42% on the first dataset, 94.95% on the second one, 93.12% on the third one and 84.00% on the last one. Recently, Febin et al [25] proposed a cascaded approach to detect violence thanks to a MoBSIFT algorithm, i.e. a mix between motion SIFT and motion boundary histogram.…”

Section: Approaches Relying On Local Descriptorsmentioning

confidence: 99%

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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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Section: Camera Motionmentioning

confidence: 99%

Section: Approaches Relying On Local Descriptorsmentioning

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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“…Xu et al [17] proposed a motion activating information retrieval technique from surveillance videos for localization guidance using optical flow maps. Febin et al [18] proposed a cascaded violence detection system for action recognition based on motion boundary SIFT (MoBSIFT) and motion filtering. Machine learning models have shown to be effective in various situations, but most overlook the importance of multiple views in battle detection in real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based System for the Detection of Human Violence in Video Data

Shoaib¹,

Sayed²

2021

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The number of security cameras positioned within the surrounding area has expanded, increasing the demand for automatic activity recognition systems. In addition to offline assessment and the issuance of an ongoing alarm in the case of aberrant behaviour, automatic activity detection systems can be employed in conjunction with human operators. In the proposed research framework, an ensemble of Mask Region-based Convolutional Neural Networks for key-point detection scheme, and LSTM based Recurrent Neural Network is used to create a deep neural network model (Mask RCNN) for recognizing violent activities (i.e. kicking, punching, etc.) of a single person. First of all, the key-points locations and ground-truth masks of humans in an image are selected using the selected region; the temporal information is extracted. Experimental results show that the ensemble model outperforms individual models. The proposed technique has a reasonable accuracy rate of 77.4 percent, 95.7 percent, and 88.2 percent, respectively, on the Weizmann, KTH, and our custom datasets. As the proposed effort applies to industry and in terms of security, it is beneficial to society.

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“…According to the two-paragraph algorithm based on film and television content and audio content, the film and television are automatically divided into a large number of film and television clips with logical semantics, and the title decoder and word indicator are added to extract text information, and the film and television are edited by indexing [9,10]. Due to the complexity of lens, the problem of lens boundary detection has not been completely solved [11]. Traditional lens boundary detection algorithms mainly include edgebased algorithm, histogram-based algorithm 45, and pixel difference method [12].…”

Section: Introductionmentioning

confidence: 99%

SIFT Feature‐Based Video Camera Boundary Detection Algorithm

Kong

2021

Complexity

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Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. Firstly, multiple frames of images are read in time sequence and converted into grayscale images. The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture. The correlation of the dynamic texture of adjacent frames and the matching degree of SIFT features of two frames were compared, and the predetection results were obtained according to the matching results. Next, compared with the next frame of the dynamic texture and SIFT feature whose step size is lower than the human eye refresh frequency, the final result is obtained. Through experiments on multiple groups of different types of film and television data, high recall rate and accuracy rate can be obtained. The algorithm in this paper can detect the gradual change lens with the complex structure and obtain high detection accuracy and recall rate. A lens boundary detection algorithm based on fuzzy clustering is realized. The algorithm can detect sudden changes/gradual changes of the lens at the same time without setting a threshold. It can effectively reduce the factors that affect lens detection, such as flash, movies, TV, and advertisements, and can reduce the influence of camera movement on the boundaries of movies and TVs. However, due to the complexity of film and television, there are still some missing and false detections in this algorithm, which need further study.

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Violence detection in videos for an intelligent surveillance system using MoBSIFT and movement filtering algorithm

Cited by 65 publications

References 28 publications

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

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

A Deep Learning Based System for the Detection of Human Violence in Video Data

SIFT Feature‐Based Video Camera Boundary Detection Algorithm

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