A Video Abnormal Behavior Recognition Algorithm Based on Deep Learning

Cao, Bin; Xia, Han-lin; Liu, Zhengyan

doi:10.1109/imcec51613.2021.9482114

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…3D convolution stacks multiple consecutive frames into a cube, and then uses the 3D convolution kernel in the composed cube to perform the operation [5]. The biggest advantage of the 3D-ConVNet structure is its speed, this encouraged many researchers to employ it in the AD area [8,12,18]. Figure 2 illustrates 3D operations.…”

Section: D Convolution Architecture (3d-convnet)mentioning

confidence: 99%

“…Environmental challenges indicate large variations in camera viewpoint and motion, cluttered background, and foreground scale variation [95]. Furthermore, changes in position, human occlusion, low-quality and noisy video, illumination changes, weather conditions, and appearances of the actors [8,56]. Some researchers tried to address these issues.…”

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%

See 2 more Smart Citations

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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Section: D Convolution Architecture (3d-convnet)mentioning

confidence: 99%

Section: Environmental Factorsmentioning

confidence: 99%

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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“…Video surveillance, fraud detection, healthcare, transportation, and industrial automation [1], [2] are just some of the areas that could benefit from efficient abnormality recognition and localization [3] in a bitstream video in the cloud computing area [4]. Other potential applications include industrial automation and healthcare [5].…”

Section: Introductionmentioning

confidence: 99%

Faster abnormality localization and recognition in a secured video bitstream by implementation of video encryption techniques

Sivalakshmi,

Prasad,

Bindu

2024

MATEC Web Conf.

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Cloud video storage uses an encrypted format to protect user data. It means encrypted video processing is an essential part of secured cloud storage. In order to detect suspicious or anomalous behavior, video surveillance must have encrypted cloud access. The primary goals of this research are to estimate parameters and detect abnormalities in an encrypted video bitstream. Various typical properties of video encoding frameworks and format-compliant encryption algorithms are investigated to identify abnormalities in an encrypted video bitstream using format-compliant encryption. The encrypted bitstream is decrypted to get three different kinds of enhancement features: the sizes of macroblocks, partitions of macroblocks, and the magnitude of the motion vector. The identification and localization methods now do not include video decryption or complete decompression. The proposed strategy has been created to implement the video encryption scheme efficiently and is compatible with various video encryption techniques. The experimental findings demonstrate that, in comparison to other methods, the proposed approach provides optimal running time and detection rate performance.

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“…Because, the spatial features only can't identify the people behaviors through time (e.g., if the player touching in an American football match is for hitting each other's, or for playing together). Hereby, the importance of second feature extraction method (i.e., temporal features extractions) comes here, which this features type is able to catch the interaction between sequential frames, to get the nature of people behavior through time [2,3].…”

Section: Introductionmentioning

confidence: 99%

Violence Detection Enhancement by Involving Convolutional Block Attention Modules Into Various Deep Learning Architectures: Comprehensive Case Study for UBI-Fights Dataset

Abbass

Kang

2023

IEEE Access

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The violence detection in surveillance videos is a complicated task, due to the requirements of extracting the spatio-temporal features in different videos environment, and various videos prospective cases. Hereby, in this paper, different architectures are proposed to perform this task in high performance, by using the UBI-Fights dataset as a comprehensive case study. The proposed architectures are based on involving the Convolutional Block Attention Modules (CBAM) with other simple layers (e.g., ConvLSTM2D or Conv2D&LSTM). In addition, using the Categorical Focal Loss (CFL) as loss function during architectures training to increase the focus on the most important features. To evaluate the proposed architectures, the performance metrics like are Area Under the Curve (AUC), and Equal Error Rate (EER); are mainly used, to declare the architecture ability of identifying the violence correctly, with low interaction value between classes. The performance results declare the ability of the proposed architectures, to achieve higher results that the state of art techniques. For example, the Conv2D&LSTM based architecture, get AUC value of 0.9493, and EER value of 0.0507; that outperform the most of the other proposed ones, and the state of art performance.

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A Video Abnormal Behavior Recognition Algorithm Based on Deep Learning

Cited by 9 publications

References 9 publications

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

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

Faster abnormality localization and recognition in a secured video bitstream by implementation of video encryption techniques

Violence Detection Enhancement by Involving Convolutional Block Attention Modules Into Various Deep Learning Architectures: Comprehensive Case Study for UBI-Fights Dataset

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