A Generalized Model for Crowd Violence Detection Focusing on Human Contour and Dynamic Features

Chexia, Zhen; Tan, Zhenhua; Wu, Danke; Ning, Jingyu; Zhang, Bin

doi:10.1109/ccgrid54584.2022.00042

Cited by 4 publications

(6 citation statements)

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“…It concludes that the proposed MobileNet model has the highest accuracy, lowest loss, and low computation time in detecting violence in the Hockey fight dataset. In their article, Paper [26] presented a model for detecting crowd violence behaviour using human contour and dynamic characteristics. The model leverages a 3D-CNN framework to extract spatial features and LSTM to combine temporal features.…”

Section: Related Workmentioning

confidence: 99%

Novel Deep Feature Fusion Framework for Multi-Scenario Violence Detection

Jebur,

Hussein,

Hoomod

et al. 2023

Computers

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Detecting violence in various scenarios is a difficult task that requires a high degree of generalisation. This includes fights in different environments such as schools, streets, and football stadiums. However, most current research on violence detection focuses on a single scenario, limiting its ability to generalise across multiple scenarios. To tackle this issue, this paper offers a new multi-scenario violence detection framework that operates in two environments: fighting in various locations and rugby stadiums. This framework has three main steps. Firstly, it uses transfer learning by employing three pre-trained models from the ImageNet dataset: Xception, Inception, and InceptionResNet. This approach enhances generalisation and prevents overfitting, as these models have already learned valuable features from a large and diverse dataset. Secondly, the framework combines features extracted from the three models through feature fusion, which improves feature representation and enhances performance. Lastly, the concatenation step combines the features of the first violence scenario with the second scenario to train a machine learning classifier, enabling the classifier to generalise across both scenarios. This concatenation framework is highly flexible, as it can incorporate multiple violence scenarios without requiring training from scratch with additional scenarios. The Fusion model, which incorporates feature fusion from multiple models, obtained an accuracy of 97.66% on the RLVS dataset and 92.89% on the Hockey dataset. The Concatenation model accomplished an accuracy of 97.64% on the RLVS and 92.41% on the Hockey datasets with just a single classifier. This is the first framework that allows for the classification of multiple violent scenarios within a single classifier. Furthermore, this framework is not limited to violence detection and can be adapted to different tasks.

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Section: Related Workmentioning

confidence: 99%

Novel Deep Feature Fusion Framework for Multi-Scenario Violence Detection

Jebur,

Hussein,

Hoomod

et al. 2023

Computers

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“…Hence, correlating data from several dimensions allows for the detection of contextual anomaly structures that may not exhibit abnormal behavior in every single dimension. ConvLSTM-based AD methods are studied in [11,13,20,40,48]. They used a CNN to learn the space features in the input image, and then fed those features into an LSTM to identify features in the temporal domain.…”

Section: Convlstm Architecturementioning

confidence: 99%

“…The data on the human skeleton is a high level of abstraction from the body and can deal with interference pretty well [39]. Specifically, the methods based on human key points are used to detect the anomalies in the video because they can effectively eliminate background noise and extract human key points in crowded video scenes [13,38]. Multiple human skeleton-based methods have been proposed for action detection and recognition, such as Openpose, Mediapipe, and Alphapose.…”

Section: Using Human Skeleton Datamentioning

confidence: 99%

“…[15] proposed a hybrid approach of distributed and centralized processing to detect abnormal behaviors of various target entities. On the other hand, human skeleton data is a high level of abstraction from the body and it has been utilized to address the problems of sparsely or moderately crowded video scenes and complex environments such as background variation, lighting changes, and noisy video [13,38].…”

Section: Environmental Factorsmentioning

confidence: 99%

“…target detection, for video streams is a vital domain in many important areas of CV, e.g., video surveillance, autonomous vehicles, robotics, virtual reality, smart cities, and medical imaging [4]. AD has been used in many areas of video surveillance to detect abnormal activities such as running, climbing, falling, fighting, and robbing [5][6][7][8][9][10]; violence, loitering, and vandalism [11][12][13][14][15]; personal intrusion [16], autism and drug addiction [17], and reckless driving [18]. It is also used to detect abnormal behavior in specific places, such as petrol stations [19] and elevators [20].…”

Section: Introductionmentioning

confidence: 99%

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Review on Deep Learning Approaches for Anomaly Event Detection in Video Surveillance

et al. 2022

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In the last few years, due to the continuous advancement of technology, human behavior detection and recognition have become important scientific research in the field of computer vision (CV). However, one of the most challenging problems in CV is anomaly detection (AD) because of the complex environment and the difficulty in extracting a particular feature that correlates with a particular event. As the number of cameras monitoring a given area increases, it will become vital to have systems capable of learning from the vast amounts of available data to identify any potential suspicious behavior. Then, the introduction of deep learning (DL) has brought new development directions for AD. In particular, DL models such as convolution neural networks (CNNs) and recurrent neural networks (RNNs) have achieved excellent performance dealing with AD tasks, as well as other challenging domains like image classification, object detection, and speech processing. In this review, we aim to present a comprehensive overview of those research methods using DL to address the AD problem. Firstly, different classifications of anomalies are introduced, and then the DL methods and architectures used for video AD are discussed and analyzed, respectively. The revised contributions have been categorized by the network type, architecture model, datasets, and performance metrics that are used to evaluate these methodologies. Moreover, several applications of video AD have been discussed. Finally, we outlined the challenges and future directions for further research in the field.

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