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A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos

John Gatara Munyua, Geoffrey Mariga Wambugu, Stephen Thiiru Njenga
2021 International Journal of Computer and Information Technology(2279-0764)  
Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos.  ...  Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats.  ...  This model was made by Zhao and others [28] in their paper named Spatial-Temporal Autoencoder for Video Anomaly Detection. Their model is composed of 3D convolutional layers.  ... 
doi:10.24203/ijcit.v10i5.166 fatcat:kbqkwer2nvh5jk6gv54xygiueq

An Improved Two-stream Inflated 3D ConvNet for Abnormal Behavior Detection

Jiahui Pan, Liangxin Liu, Mianfen Lin, Shengzhou Luo, Chengju Zhou, Huijian Liao, Fei Wang
2021 Intelligent Automation and Soft Computing  
Abnormal behavior detection is an essential step in a wide range of application domains, such as smart video surveillance.  ...  The proposed approach consists of four parts: (1) preprocessing pretreatment for the input video; (2) dynamic feature extraction from video streams using a two-stream inflated 3D (I3D) ConvNet network;  ...  In the convolutional layer, 3D convolution (3D Conv) is better able to capture spatial and temporal information in videos than 2D convolution.  ... 
doi:10.32604/iasc.2021.020240 fatcat:c6cuxunx3vcltbcsbtr2aar2cu

An On-Line and Adaptive Method for Detecting Abnormal Events in Videos Using Spatio-Temporal ConvNet

Samir Bouindour, Hichem Snoussi, Mohamad Hittawe, Nacef Tazi, Tian Wang
2019 Applied Sciences  
We address in this paper the problem of abnormal event detection in video-surveillance. In this context, we use only normal events as training samples.  ...  We propose to use a modified version of pretrained 3D residual convolutional network to extract spatio-temporal features, and we develop a robust classifier based on the selection of vectors of interest  ...  Acknowledgments: The authors are grateful to anonymous reviewers for their comments that considerably enhanced the quality of the paper.  ... 
doi:10.3390/app9040757 fatcat:x6qylek4hvh2rogz3jcq2jsc2m

Weapon Detection Using YOLO V3 for Smart Surveillance System

Sanam Narejo, Bishwajeet Pandey, Doris Esenarro vargas, Ciro Rodriguez, M. Rizwan Anjum, Zain Anwar Ali
2021 Mathematical Problems in Engineering  
Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing.  ...  Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.  ...  As the first step for any video surveillance application, object detection and classification are essential for further object tracking tasks.  ... 
doi:10.1155/2021/9975700 fatcat:mf66ecbr5raldbf63frihh2oty

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder [article]

Yong Shean Chong, Yong Haur Tay
2017 arXiv   pre-print
We present an efficient method for detecting anomalies in videos.  ...  However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes.  ...  The authors of [33] applied a 3D ConvNet on classifying anomalies, whereas [5] used an end-to-end convolutional autoencoder to detect anomalies in surveillance videos.  ... 
arXiv:1701.01546v1 fatcat:iaqu7vawfzgwdilbo7ws4sfnje

An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders

Ming Xu, Xiaosheng Yu, Dongyue Chen, Chengdong Wu, Yang Jiang
2019 Applied Sciences  
Anomaly detection in crowded scenes is an important and challenging part of the intelligent video surveillance system.  ...  In this paper, we propose a new baseline framework of anomaly detection for complex surveillance scenes based on a variational auto-encoder with convolution kernels to learn feature representations.  ...  Acknowledgments: We are thankful to anonymous editor and reviewers for their valuable comments and kind suggestions in early versions of this article.  ... 
doi:10.3390/app9163337 fatcat:xxczp3visne6xdnwac7yhrgjlm

Anomalous Human Activity Recognition in Surveillance Videos

2019 International journal of recent technology and engineering  
So, the paper has two parts that include adaptive video compression approaches of the surveillance videos and providing that compressed video as the input to detect anomalous human activity  ...  The objective of the discussion is to be able to implement an automated anomalous human activity recognition system which uses surveillance video to capture the occurrence of an unusual event and caution  ...  Therefore, this solution for anomaly detection uses the idea of combining the temporal sequencer with the spatial feature extractor, resulting in development of end to end model that is trainable for anomaly  ... 
doi:10.35940/ijrte.b1064.0782s719 fatcat:352q6ou655dr7oj2jtk2rl6eca

A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras

Soheil Vosta, Kin-Choong Yow
2022 Applied Sciences  
Our future work focuses on adding an attention layer to the existing model to detect more abnormal events.  ...  As a result, if any anomalous happens in front of the surveillance cameras, it can be detected immediately.  ...  [29] presented a model for learning spatiotemporal features with 3D convolutional networks.  ... 
doi:10.3390/app12031021 fatcat:t2rcpamdknaiphwokrw2t5tlne

DeepFall – Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders [article]

Jacob Nogas, Shehroz S. Khan, Alex Mihailidis
2020 arXiv   pre-print
In this paper, we present a novel framework, DeepFall, which formulates the fall detection problem as an anomaly detection problem.  ...  The DeepFall framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities.  ...  Chong and Tay [8] present a method to detect anomalies in videos that consists of a spatial feature extractor and a temporal encoder-decoder framework.  ... 
arXiv:1809.00977v3 fatcat:pkx74dyumfbtvof7yzmctr34c4

Deep anomaly detection through visual attention in surveillance videos

Nasaruddin Nasaruddin, Kahlil Muchtar, Afdhal Afdhal, Alvin Prayuda Juniarta Dwiyantoro
2020 Journal of Big Data  
This paper describes a method for learning anomaly behavior in the video by finding an attention region from spatiotemporal information, in contrast to the full-frame learning.  ...  Our system is trained and tested against a large-scale UCF-Crime anomaly dataset for validating its effectiveness.  ...  As illustrated in Fig. 6 , for its good performance and efficiency, the popular Convolutional 3D Networks (C3D) [43] is selected as our pre-trained feature extractor.  ... 
doi:10.1186/s40537-020-00365-y fatcat:cn2dsuzxlrg7db3qyqlxqihvia

IBaggedFCNet: An Ensemble Framework for Anomaly Detection in Surveillance Videos

Yumna Zahid, Muhammad A. Tahir, Muhammad N. Durrani, Ahmed Bouridane
2020 IEEE Access  
They have investigated a 3D convolutional neural network proposed by Tran et al.  ...  For normal instances, as : Segment-level (60 frames) anomaly values, as detected using Inception_v3 feature extraction performed on some test videos from UCF-Anomaly Detection dataset [11] , are shown  ... 
doi:10.1109/access.2020.3042222 fatcat:ebflecvnszfr5beyy6q5xb2kxa

Anomaly Locality in Video Surveillance [article]

Federico Landi, Cees G. M. Snoek, Rita Cucchiara
2019 arXiv   pre-print
This paper strives for the detection of real-world anomalies such as burglaries and assaults in surveillance videos.  ...  For this purpose, we enrich existing surveillance videos with spatial and temporal annotations: it is the first dataset for anomaly detection with bounding box supervision in both its train and test set  ...  [1] use C3D [21] , a 3D Con-vNet, to extract features from a starting video segment.  ... 
arXiv:1901.10364v1 fatcat:dy4rrc7pdfcwbjjlg5j5sks2g4

Anomaly Detection in Video Sequences: A Benchmark and Computational Model [article]

Boyang Wan and Wenhui Jiang and Yuming Fang and Zhiyuan Luo and Guanqun Ding
2021 arXiv   pre-print
To tackle these problems, we contribute a new Large-scale Anomaly Detection (LAD) database as the benchmark for anomaly detection in video sequences, which is featured in two aspects. 1) It contains 2000  ...  We first obtain the local spatiotemporal contextual feature by using an Inflated 3D convolutional (I3D) network.  ...  Zha, “Learning to detect anomalies in surveillance tion for video anomaly detection with a few anomalies,” in International Joint video,” IEEE Signal Processing Letters, vol. 22, no. 9, pp  ... 
arXiv:2106.08570v1 fatcat:oeju32uh65fpliiewsfmhupu44

Video Processing using Deep learning Techniques: A Systematic Literature Review

Vijeta Sharma, Manjari Gupta, Ajai Kumar, Deepti Mishra
2021 IEEE Access  
The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis.  ...  Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc.  ...  This method combines CNN as a feature extractor and LSTM for classification. [105] explain the anomaly detection in realtime videos by using optical-flow convolutional autoencoder and convolutional LSTM  ... 
doi:10.1109/access.2021.3118541 fatcat:oadlu4uyirc2tanqrixz3sn6ny

Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild

Mohammad Ibrahim Sarker, Cristina Losada-Gutiérrez, Marta Marrón-Romera, David Fuentes-Jiménez, Sara Luengo-Sánchez
2021 Sensors  
Spatio-temporal features are extracted from each surveillance video using a temporal convolutional 3D neural network (T-C3D).  ...  In this work, it is proposed an approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm.  ...  features are extracted for each video segment using a T-C3D [30] as a feature extractor.  ... 
doi:10.3390/s21123993 pmid:34207883 pmcid:PMC8230050 fatcat:kvxd3ujt3rfohf5qg4s3eb4hzm
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