Deep-violence: individual person violent activity detection in video

Naik, Anuja Jana; Gopalakrishna, M. T.

doi:10.1007/s11042-021-10682-w

Cited by 29 publications

(18 citation statements)

References 25 publications

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“…To identify violent behaviors of a single person, an ensemble model of the Mask RCNN and LSTM was proposed ( Naik & Gopalakrishna, 2021 ). Initially, human key points and masks were extracted, and then temporal information was captured.…”

Section: Classification Of Violence Detection Techniquesmentioning

confidence: 99%

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

Omarov

Narynov²,

Zhumanov

et al. 2022

PeerJ Computer Science

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We investigate and analyze methods to violence detection in this study to completely disassemble the present condition and anticipate the emerging trends of violence discovery research. In this systematic review, we provide a comprehensive assessment of the video violence detection problems that have been described in state-of-the-art researches. This work aims to address the problems as state-of-the-art methods in video violence detection, datasets to develop and train real-time video violence detection frameworks, discuss and identify open issues in the given problem. In this study, we analyzed 80 research papers that have been selected from 154 research papers after identification, screening, and eligibility phases. As the research sources, we used five digital libraries and three high ranked computer vision conferences that were published between 2015 and 2021. We begin by briefly introducing core idea and problems of video-based violence detection; after that, we divided current techniques into three categories based on their methodologies: conventional methods, end-to-end deep learning-based methods, and machine learning-based methods. Finally, we present public datasets for testing video based violence detectionmethods’ performance and compare their results. In addition, we summarize the open issues in violence detection in videoand evaluate its future tendencies.

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Section: Classification Of Violence Detection Techniquesmentioning

confidence: 99%

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

Omarov

Narynov²,

Zhumanov

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Nevertheless, unlike edge learning, the collected features may also be fed into support vector machines (SVM) and other shallow model classifiers as input. Utilizing features through handcrafted feature descriptors and providing them to a deep classifier is another method to build deep models [18]. [19].…”

Section: Related Workmentioning

confidence: 99%

A Skeleton-based Approach for Campus Violence Detection

Omarov¹,

Narynov²,

Zhumanov³

et al. 2022

Computers, Materials &Amp; Continua

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In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate highprocessing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person's activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were generated. Finally, the proposed method was trained and tested using the collected dataset. The results indicate the accuracy of 97% was achieved in identifying aggressive conduct in video sequences. Furthermore, the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.

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“…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%

“…Many key factors must be taken into account when building a dataset, such as the availability of labeled data, activity type, size of samples, test environments, the diversity of the captured video, etc. Most researchers divide a dataset into two groups, training data and testing data, with certain percentages for each group, such as 70% and 30% [6,56] or 80% and 20% [11,17,38] from samples for training and testing, respectively. Few researchers divide their dataset into three sections: training, testing, and validation.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

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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Deep-violence: individual person violent activity detection in video

Cited by 29 publications

References 25 publications

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

A Skeleton-based Approach for Campus Violence Detection

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

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