Deep Reinforcement Learning for Real-world Anomaly Detection in Surveillance Videos

Aberkane, Sabrina Messaoud; Elarbi, Mohamed

doi:10.1109/ispa48434.2019.8966795

Cited by 15 publications

(8 citation statements)

References 19 publications

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“…In particular, the DRL-based AD approach leverages the labeled anomaly data to increase the detection accuracy without restricting the set of anomalies sought to those provided as anomalous examples. The approach accomplishes this by interacting with an environment created using the training data [35,36].…”

Section: Deep Reinforcement Learning-based Admentioning

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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Section: Deep Reinforcement Learning-based Admentioning

confidence: 99%

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

et al. 2022

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“…In surveillance video, the primary action is frequently identified as commonplace, unproblematic behavior. A smart video surveillance system's more critical and challenging task is to locate and detect anomalous actions that are predicted to occur with a lower likelihood than regular activity [32]. Public security was greatly enhanced by smart video surveillance, which used computer vision algorithms to analyze and comprehend the longer video stream.…”

Section: B Drl For Anomaly Detection In Different Domains 1) Vedio An...mentioning

confidence: 99%

Deep Reinforcement Learning for Anomaly Detection: A Systematic Review

et al. 2022

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Anomaly detection has been used to detect and analyze anomalous elements from data for years. Various techniques have been developed to detect anomalies. However, the most convenient one is Machine learning which is performing well but still has limitations for large-scale unlabeled datasets. Deep Reinforcement Learning (DRL) based techniques outperform the existing supervised or unsupervised and other alternative techniques for anomaly detection. This study presents a Systematic Literature Review (SLR), which analyzes DRL models that detect anomalies in their application. This SLR aims to analyze the DRL frameworks for anomaly detection applications, proposed DRL methods, and their performance comparisons against alternative methods. In this review, we have identified 32 research articles from 2017-2022 that discuss DRL techniques for various anomaly detection applications. After analyzing the selected research articles, this paper presents 13 different applications of anomaly detection found in the selected research articles. We identified 50 different datasets applied in experiments on anomaly detection and demonstrated 17 distinct DRL models used in the selected papers to detect anomalies. Finally, we analyzed the performance of these DRL models and reviewed them. Additionally, we observed that detecting anomalies using DRL frameworks is a promising area of research and showed that DRL had shown better performance for anomaly detection where other models lack. Therefore, we provide researchers with recommendations and guidelines based on this review.

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“…The third step involves the agent sending the action to the environment and finally, it modifies its internal state as inspired by the previous state and agents' actions [36]. This learning technique has been applied by Aberkane [37] to detect anomalies in videos. Aberkane and Elarbi used a Deep Q Learning Network (DQN) to locate anomalies in videos.…”

Section: E Reinforcement Learningmentioning

confidence: 99%

“…The DQN enables the agent to learn how anomalies are detected and recognized in the videos. DQN is composed of a fully connected layer, that calculates the probability of every video clip in the anomalous and normal bags demonstrating the likelihood of a clip containing an anomaly [37]. F1 Score was used in some papers.…”

Section: E Reinforcement Learningmentioning

confidence: 99%

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

Munyua

Wambugu

Njenga

2021

IJCIT

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Deep learning has proven to be a landmark computing approach to the computer vision domain. 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. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well.

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Deep Reinforcement Learning for Real-world Anomaly Detection in Surveillance Videos

Cited by 15 publications

References 19 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

Deep Reinforcement Learning for Anomaly Detection: A Systematic Review

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

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