An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders

Mei, Xu; Yu, Xiaoqing; Chen, Dongyue; Wu, Chengdong; Jiang, Yang

doi:10.3390/app9163337

Cited by 38 publications

(14 citation statements)

References 42 publications

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“…It reports the average running time of each frame during the test phase. Our method is significantly faster than MDT [ 6 ], AMDN [ 3 ], Xu et al’s method without GPU [ 61 ], and Hierarchical framework [ 62 ]. Our method is also faster than ST-CNN [ 63 ], AED [ 64 ], and ICN [ 65 ].…”

Section: Experimental Results and Evaluationmentioning

confidence: 99%

An explainable and efficient deep learning framework for video anomaly detection

Shao

Tunc

et al. 2021

Cluster Comput

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Section: Experimental Results and Evaluationmentioning

confidence: 99%

An explainable and efficient deep learning framework for video anomaly detection

Shao

Tunc

et al. 2021

Cluster Comput

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“…A wheelchair is also considered an anomaly in a different scene. The dataset is divided into two subsets and each video contains different clips [29]. The video segment contains 200 frames in each clip of the video which is almost 6 to 7 s of time duration.…”

Section: Methodsmentioning

confidence: 99%

Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Masood¹,

Al-Sakhnini²,

Nawaz³

et al. 2023

Computers, Materials &Amp; Continua

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Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and healthcare. Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies. The Graph-based deep learning networks are designed to predict unknown objects and outliers. In our case, they detect unusual objects in the form of malicious nodes. The edges between nodes represent a relationship of nodes among each other. In case of anomaly, such as the bike rider in Pedestrians data, the rider node has a negative value for the edge and it is identified as an anomaly. The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome. Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities, which shows a huge potential in automatically monitoring surveillance videos. Performing autonomous monitoring of CCTV, crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places. The suggested GNN model improves accuracy by 4% for the Pedestrian 5418 CMC, 2023, vol.74, no.3 2 dataset and 12% for the Pedestrian 1 dataset compared to a few state-ofthe-art techniques.

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“…In this way, one branch is in charge of frame reconstruction (capturing spatial dependency) and the other one attempts to estimate the optical flow map, to capture motion dependency, customized for the task. Different models that have been applied for representation learning or reconstruction-based anomaly detection are as follows: PCA [67], classic AE [62], Conv-AE [62], Contractive-AE [68], Conv-LSTM-AE [17], [22], Hybrid Spatio-Temporal Autoencoder [69], Denoising AEs [70] and VAE [71], GRU-AE [72]. Some of the other examples in this field are [57], [73], [74], [75], [76].…”

Section: Reconstruction-based Methodsmentioning

confidence: 99%

A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods

Mohammad¹,

Bergevin²

2021

Preprint

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Video anomaly detection is one of the hot research topics in computer vision nowadays, as abnormal events contain a high amount of information. Anomalies are one of the main detection targets in surveillance systems, usually needing real-time actions. Regarding the availability of labeled data for training (i.e., there is not enough labeled data for abnormalities), semi-supervised anomaly detection approaches have gained interest recently. This paper introduces the researchers of the field to a new perspective and reviews the recent deep-learning based semi-supervised video anomaly detection approaches, based on a common strategy they use for anomaly detection. Our goal is to help researchers develop more effective video anomaly detection methods. As the selection of a right Deep Neural Network plays an important role for several parts of this task, a quick comparative review on DNNs is prepared first. Unlike previous surveys, DNNs are reviewed from a spatiotemporal feature extraction viewpoint, customized for video anomaly detection. This part of the review can help researchers in this field select suitable networks for different parts of their methods. Moreover, some of the state-of-the-art anomaly detection methods, based on their detection strategy, are critically surveyed. The review provides a novel and deep look at existing methods and results in stating the shortcomings of these approaches, which can be a hint for future works.

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An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders

Cited by 38 publications

References 42 publications

An explainable and efficient deep learning framework for video anomaly detection

An explainable and efficient deep learning framework for video anomaly detection

Identification of Anomaly Scenes in Videos Using Graph Neural Networks

A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods

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