A classification method based on optical flow for violence detection

Mahmoodi, Javad; Salajeghe, Afsane

doi:10.1016/j.eswa.2019.02.032

Cited by 63 publications

(30 citation statements)

References 12 publications

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“…Gao et al [7] proposed an oriented ViF descriptor that utilises the orientation of the optical flow information, which was not considered by the ViF. Recently, Mahmoodi et al [16] proposed a method that computes the optical flow between sequential frames and compares the magnitude and orientation of each pixel in each frame to the global optical flow to obtain the changes in orientation and magnitude.…”

Section: Related Workmentioning

confidence: 99%

A Crowd Analysis Framework for Detecting Violence Scenes

Gkountakos

Ioannidis

Tsikrika

et al. 2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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This work examines violence detection in video scenes of crowds and proposes a crowd violence detection framework based on a 3D convolutional deep learning architecture, the 3D-ResNet model with 50 layers. The proposed framework is evaluated on the Violent Flows dataset against several state-of-the-art approaches and achieves higher accuracy values in almost all occasions, while also performing the violence detection activities in (near) real-time.

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Section: Related Workmentioning

confidence: 99%

A Crowd Analysis Framework for Detecting Violence Scenes

Gkountakos

Ioannidis

Tsikrika

et al. 2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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“…Such approach reached high accuracies on different datasets: 95.1%, 100% and 94.31%. Mahmoodi et al [69] proposed HOMO (Histogram of Optical flow Magnitude and Orientation) descriptor to identify violent behaviour in both crowded and uncrowded situations, and a SVM classifier was adopted to get the classification. Accuracy rates of this approach are generally satisfying: (89.3% and 76.83% for the first and second dataset).…”

Section: Approaches Using Optical Flow 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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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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“…However, this method performed poorly in crowded scenes. Recently, Mahmoodi et al [19] proposed a new feature descriptor named Histogram of Optical flow Magnitude and Orientation (HOMO) to improve existing violence detection. Xu et al [20] proposed a localization guided framework which exploits optical flow maps to extract motion activation information for detecting fight actions in surveillance videos.…”

Section: Introductionmentioning

confidence: 99%

Motion Direction Inconsistency‐Based Fight Detection for Multiview Surveillance Videos

Chong

Wang

et al. 2021

Wireless Communications and Mobile Computing

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Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

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A classification method based on optical flow for violence detection

Cited by 63 publications

References 12 publications

A Crowd Analysis Framework for Detecting Violence Scenes

A Crowd Analysis Framework for Detecting Violence Scenes

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

Motion Direction Inconsistency‐Based Fight Detection for Multiview Surveillance Videos

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