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مراجعة حول اکتشاف الأحداث الشاذة والتعرف علیها

منار دنیا, وسام البهیدى, علیاء یوسف
2021 النشرة المعلوماتیة فی الحاسبات والمعلومات  
Recently, researches depend on one of two techniques: handcrafted features and deep learning models.  ...  This paper presents a survey on both handcrafted and deep learning models for abnormal events detection.  ...  event detection based on appearance and motion information [10] Crowded Violence detection based on motion weber local descriptor (MoWLD)[11] Crowded Violence detection based on spatio-temporal  ... 
doi:10.21608/fcihib.2020.47617.1004 fatcat:huqdqx64cfgzlh3jkzmgsoqjaq

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

G. Sreenu, M. A. Saleem Durai
2019 Journal of Big Data  
Majority of the papers reviewed in this survey are based on deep learning technique. Various deep learning methods are compared in terms of their algorithms and models.  ...  Manual surveillance seems tedious and time consuming. Security can be defined in different terms in different contexts like theft identification, violence detection, chances of explosion etc.  ...  Spatio-temporal texture modeling for real-time crowd anomaly detection [105] . Spatio temporal texture is a combination of spatio temporal slices and spatio temporal volumes.  ... 
doi:10.1186/s40537-019-0212-5 fatcat:mh7d5d5c5zeczf5sdmgwz3claq

ViolenceNet: Dense Multi-Head Self-Attention with Bidirectional Convolutional LSTM for Detecting Violence

Fernando J. Rendón-Segador, Juan A. Álvarez-García, Fernando Enríquez, Oscar Deniz
2021 Electronics  
memory (LSTM) module, that allows encoding relevant spatio-temporal features, to determine whether a video is violent or not.  ...  the most complex dataset to 100% on the simplest one) and inference time (less than 0.3 s for the longest clips).  ...  The data presented in this study is openly available in https://github.com/FernandoJRS/violence-detection-deeplearning Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics10131601 fatcat:xac2hbengnbc3fwph2vjubl3yu

Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM [article]

Zahidul Islam, Mohammad Rukonuzzaman, Raiyan Ahmed, Md. Hasanul Kabir, Moshiur Farazi
2021 arXiv   pre-print
SepConvLSTM is constructed by replacing convolution operation at each gate of ConvLSTM with a depthwise separable convolution that enables producing robust long-range Spatio-temporal features while using  ...  Automatically detecting violence from surveillance footage is a subset of activity recognition that deserves special attention because of its wide applicability in unmanned security monitoring systems,  ...  Once all the frames are passed we extract the Spatio-temporal features from the hidden state of the last time-step of the LSTM.  ... 
arXiv:2102.10590v3 fatcat:7pkwozvzuzczdhehi3a7gcnaie

Violence Detection In Surveillance Videos Using Deep Learning

Mostafa Mohamed Moaaz, Ensaf Hussein Mohamed
2020 النشرة المعلوماتیة فی الحاسبات والمعلومات  
It extracts a set of selectively distributed frames of the video clip, performs spatio-temporal features, and passes them to a fully connected neural to classify the video to violence or non-violence action  ...  The model is evaluated on different datasets; like Real Life Violence Situations aka RLVS and Hockey Fight Detection datasets.  ...  Spatial feature extraction using the convolutional neural network. Temporal feature extraction using LSTM that output the Spatio-temporal features from the frames.  ... 
doi:10.21608/fcihib.2020.42233.1003 fatcat:4wdqdpel2rbonbc5rqsh4vt2xe

Deep Learning Approach for Violence Detection in Urban Areas

Marius Baba, Vasile Gui, Dan Pescaru, N. Herisanu, M. Razzaghi, R. Vasiu
2019 ITM Web of Conferences  
The second approach uses deep learning techniques. They demonstrate outstanding performances in image and actions classification based on a prior learning process.  ...  By combining these two approaches we succeed to obtain a real-time and cost-effective solution designed for urban area surveillance networks.  ...  Capturing violence actions in real time by surveillance system implies constant analysis applied on video streams.  ... 
doi:10.1051/itmconf/20192903009 fatcat:i2ogf4tjqfgjne2khylp4h54zy

A Review on state-of-the-art Violence Detection Techniques

Muhammad Ramzan, Adnan Abid, Hikmat Ullah Khan, Shahid Mahmood Awan, Amina Ismail, Muzamil Ahmed, Mahwish Ilyas, Ahsan Mahmood
2019 IEEE Access  
In this paper, the methods of detection are divided into three categories that is based on classification techniques used: violence detection using traditional machine learning, using support vector machine  ...  INDEX TERMS Violence detection, violent behavior, support vector machine, deep learning, machine learning, surveillance camera, computer vision. 107560 This work is licensed under a Creative Commons Attribution  ...  single-temporal methods. 10) REAL TIME VIOLENCE DETECTION To reliably detect violence actions, manually-selected features are insufficient typically.  ... 
doi:10.1109/access.2019.2932114 fatcat:v5bkozfisneprkvikxpi4scnkq

Contributions to the Problem of Fight Detection in Video

Ismael Serrano-Gracia
2018 ELCVIA Electronic Letters on Computer Vision and Image Analysis  
While action detection has become an important line of research in computer vision, the detection of particular events such as violence, aggression or fights, has been relatively less studied.  ...  The clear practical applications have led to a surge of interest in developing violence detectors.  ...  These reviewed categories are: spatial and spatio-temporal descriptors, optical flow, trajectories, deep learning, use of audio and others.  ... 
doi:10.5565/rev/elcvia.1135 fatcat:laveb4lzpbcopeue5a5yhn2664

State-of-the-Art Violence Detection Techniques: A review

Milon Biswas, Afjal Hossain Jibon, Mim Kabir, Khandokar Mohima, Rahman Sinthy, Md. Shamsul Islam, Monowara Siddique
2022 Asian Journal of Research in Computer Science  
So, researchers are doing a lot of research on different techniques for detecting violence.  ...  The method of detection is divided into three categories. These categories are based on the classification techniques used.  ...  Autocorrelation of Gradients-based Violence Detection This framework utilized the Spatio-temporal autocorrelation of gradient-based features to effectively detect violent activities in crowded scenes.  ... 
doi:10.9734/ajrcos/2022/v13i130305 fatcat:us54wnmflrho3nnuijn4ofzmme

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

Wafa Lejmi, Anouar Ben Khalifa, Mohamed Ali Mahjoub
2020 Traitement du signal  
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 obtained results are promising and show that the proposed model can be potentially useful for detecting human violence.  ...  spatio-temporal ones.  ... 
doi:10.18280/ts.370501 fatcat:wtqbfu3jcvawjlnjvebpol6piq

A Sensor Network Approach for Violence Detection in Smart Cities Using Deep Learning

Marius Baba, Vasile Gui, Cosmin Cernazanu, Dan Pescaru
2019 Sensors  
Known solutions to real-time violence detection are not suitable for implementation in a resource-constrained environment due to the high processing power requirements.  ...  Our algorithm achieves real-time processing on a Raspberry PI-embedded architecture.  ...  Therefore, we achieved to design a real time distributed system able to detect violence in urban areas. It is based on Raspberry PI nodes running the algorithm depicted in Figure 1 .  ... 
doi:10.3390/s19071676 fatcat:5unqafw3zrb35plyfwnqyqms2q

Efficient Spatio-Temporal Modeling Methods for Real-Time Violence Recognition

Min-seok Kang, Rae-Hong Park, Hyung-Min Park
2021 IEEE Access  
The proposed modules (MSM and T-SE block) are lightweight for on-device real-time violence recognition but consistently improve detection performance.  ...  CONCLUSION We proposed spatio-temporal attention modules and framegrouping method to build a practical violence detection system.  ... 
doi:10.1109/access.2021.3083273 fatcat:uej7vemjkbcu7pzwlcwe2rwzvy

Detecting Abnormal Activities using Computer Vision in Big Data Framework

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Various techniques can be used in the field of computer vision feature extraction and description scheme.  ...  The abnormal behaviour of any person can be detected using computer vision.  ...  data real time analytics using IOT and cyber war OGMM(optical flow as low level localize the position of 6 [6] 2016 feature and quantizes the abnormal with characteristic GMM, big data real time data  ... 
doi:10.35940/ijitee.j1013.08810s219 fatcat:nidixlsl7nf3zdgq4nfaix3nyq

A Crowd Analysis Framework for Detecting Violence Scenes

Konstantinos Gkountakos, Konstantinos Ioannidis, Theodora Tsikrika, Stefanos Vrochidis, Ioannis Kompatsiaris
2020 Zenodo  
detection activities in (near) real-time.  ...  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  ...  [14] generated a spatio-temporal feature extractor based on optical flow features. Zhou et al.  ... 
doi:10.5281/zenodo.3751000 fatcat:cplakq7llbe5jaufm2ytfm5ssy

Moment Features based Violence Action Detection using Optical Flow

A F M Saifuddin Saif, Zainal Rasyid
2020 International Journal of Advanced Computer Science and Applications  
The severity of injury causes due to violence can be minimized by detecting violence in real time demands for effective violence detection.  ...  This research proposes an efficient method for violence detection using moment features to use motion patterns to facilitate detection in each frame and provides smaller area as region of interest.  ...  However, due to usage of a custom build convolutional neural network and long short term memory LSTM recurrent neural network to process spatio-temporal features based on space and time dimensions for  ... 
doi:10.14569/ijacsa.2020.0111163 fatcat:7kipa2k42jbrxh7rxegmku5oie
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