Detection of violent crowd behavior based on statistical characteristics of the optical flow

Huang, Jianfeng; Chen, Shui-Li

doi:10.1109/fskd.2014.6980896

Cited by 24 publications

(12 citation statements)

References 20 publications

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“…Huang, J.F. et al [19] analyzed the behavior of a violent crowd. They present a method that only measured the statistical property in video frames, and then they used a support vector machine to discriminate the video frames into two classes, i.e., normal and abnormal.…”

Section: Introductionmentioning

confidence: 99%

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

et al. 2019

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Movies have become one of the major sources of entertainment in the current era, which are based on diverse ideas. Action movies have received the most attention in last few years, which contain violent scenes, because it is one of the undesirable features for some individuals that is used to create charm and fantasy. However, these violent scenes have had a negative impact on kids, and they are not comfortable even for mature age people. The best way to stop under aged people from watching violent scenes in movies is to eliminate these scenes. In this paper, we proposed a violence detection scheme for movies that is comprised of three steps. First, the entire movie is segmented into shots, and then a representative frame from each shot is selected based on the level of saliency. Next, these selected frames are passed from a light-weight deep learning model, which is fine-tuned using a transfer learning approach to classify violence and non-violence shots in a movie. Finally, all the non-violence scenes are merged in a sequence to generate a violence-free movie that can be watched by children and as well violence paranoid people. The proposed model is evaluated on three violence benchmark datasets, and it is experimentally proved that the proposed scheme provides a fast and accurate detection of violent scenes in movies compared to the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

et al. 2019

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“…Zhou et al [31] generated low level descriptors by extracting features from regions characterised by higher values of optical flow. Furthermore, Huang et al [11] performed violent crowd behaviour analysis by considering only the statistical properties of the optical flow field in video data and performed classification using SVMs. Zhang et al [30] presented a violence detection framework from surveillance video streams based on a Gaussian model of optical flow; they extracted violence optical flow vectors and also used SVMs for the classification.…”

Section: Related Workmentioning

confidence: 99%

“…Violent Flows (A+SD) Type HNF [12] 56.52 + 0.33 HC HNF + BoW [19] 57.05 + 0.32 HC MoSIFT + BoW [19] 57.09 + 0.37 HC HOG [12] 57.43 + 0.37 HC HOG + BoW [19] 57.98 + 0.37 HC HOF [12] 58,53 + 0.32 HC HOF+ BoW [19] 58.71 + 0.12 HC LTP [26] 71,53 + 0.17 HC OViF [10] 76.80 + 3.90 HC ViF [10] 81.30 + 0.21 HC GMOF [30] 82.79 HC AMDN [24] 84.72 + 0.17 DL Substantial Derivative [17] 85.53 + 0.21 HC DiMOLIF [14] 85.83 HC SCOF [11] 86.37 HC ViF+OViF [7] 88.00 + 2.45 HC MoWLD+BoW [19] 88.16 + 0.19 HC MoSIFT+KDE+Sparse Coding [25] 89.05 + 3.26 HC MoWLD + Sparce Coding [29] 89.38 + 0.13 HC PSS [1] 89.50 + 0.13 HC SSS [23] 91.90 + 0.12 HC Spatiotemporal Encoder [8] 92.18 + 3.29 DL SSDLSC [27] 92.25 + 0.12 HC MoIWlD [28] 93.19 + 0.12 HC LHOG+LHOF+BoW [31] 94.31 + 1.65 HC STIFV [2] 96.40 (mean) HC Ullah et al [22] 98.00 DL…”

Section: Acronym/abbreviationmentioning

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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“…This approach is computationally effective to detect violence in crowded contexts and its accuracy rate is impressive (around 82.9%). Huang et al [66] studied the optical flow and found that its variance change increases in case of a crowd violence. Therefore, they suggested a statistical approach relying on optical flow fields to detect the behaviours of a violent crowd.…”

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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Detection of violent crowd behavior based on statistical characteristics of the optical flow

Cited by 24 publications

References 20 publications

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

A Crowd Analysis Framework for Detecting Violence Scenes

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

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