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Journal articles on the topic 'VIDEO ANOMALY DETECTION'

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Author: Grafiati
Published: 11 September 2023

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1

Zhang, Yuxing, Jinchen Song, Yuehan Jiang, and Hongjun Li. "Online Video Anomaly Detection." Sensors 23, no. 17 (August 26, 2023): 7442. http://dx.doi.org/10.3390/s23177442.

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With the popularity of video surveillance technology, people are paying more and more attention to how to detect abnormal states or events in videos in time. Therefore, real-time, automatic and accurate detection of abnormal events has become the main goal of video-based surveillance systems. To achieve this goal, many researchers have conducted in-depth research on online video anomaly detection. This paper presents the background of the research in this field and briefly explains the research methods of offline video anomaly detection. Then, we sort out and classify the research methods of online video anomaly detection and expound on the basic ideas and characteristics of each method. In addition, we summarize the datasets commonly used in online video anomaly detection and compare and analyze the performance of the current mainstream algorithms according to the evaluation criteria of each dataset. Finally, we summarize the future trends in the field of online video anomaly detection.
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de Paula, Davi D., Denis H. P. Salvadeo, and Darlan M. N. de Araujo. "CamNuvem: A Robbery Dataset for Video Anomaly Detection." Sensors 22, no. 24 (December 19, 2022): 10016. http://dx.doi.org/10.3390/s222410016.

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(1) Background: The research area of video surveillance anomaly detection aims to automatically detect the moment when a video surveillance camera captures something that does not fit the normal pattern. This is a difficult task, but it is important to automate, improve, and lower the cost of the detection of crimes and other accidents. The UCF–Crime dataset is currently the most realistic crime dataset, and it contains hundreds of videos distributed in several categories; it includes a robbery category, which contains videos of people stealing material goods using violence, but this category only includes a few videos. (2) Methods: This work focuses only on the robbery category, presenting a new weakly labelled dataset that contains 486 new real–world robbery surveillance videos acquired from public sources. (3) Results: We have modified and applied three state–of–the–art video surveillance anomaly detection methods to create a benchmark for future studies. We showed that in the best scenario, taking into account only the anomaly videos in our dataset, the best method achieved an AUC of 66.35%. When all anomaly and normal videos were taken into account, the best method achieved an AUC of 88.75%. (4) Conclusion: This result shows that there is a huge research opportunity to create new methods and approaches that can improve robbery detection in video surveillance.
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Duong, Huu-Thanh, Viet-Tuan Le, and Vinh Truong Hoang. "Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey." Sensors 23, no. 11 (May 24, 2023): 5024. http://dx.doi.org/10.3390/s23115024.

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Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. There has been a variety of surveys of anomaly detection, such as of network anomaly detection, financial fraud detection, human behavioral analysis, and many more. Deep learning has been successfully applied to many aspects of computer vision. In particular, the strong growth of generative models means that these are the main techniques used in the proposed methods. This paper aims to provide a comprehensive review of the deep learning-based techniques used in the field of video anomaly detection. Specifically, deep learning-based approaches have been categorized into different methods by their objectives and learning metrics. Additionally, preprocessing and feature engineering techniques are discussed thoroughly for the vision-based domain. This paper also describes the benchmark databases used in training and detecting abnormal human behavior. Finally, the common challenges in video surveillance are discussed, to offer some possible solutions and directions for future research.
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Monakhov, Vladimir, Vajira Thambawita, Pål Halvorsen, and Michael A. Riegler. "GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos." Sensors 23, no. 4 (February 13, 2023): 2087. http://dx.doi.org/10.3390/s23042087.

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The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos.
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Yuan, Hongchun, Zhenyu Cai, Hui Zhou, Yue Wang, and Xiangzhi Chen. "TransAnomaly: Video Anomaly Detection Using Video Vision Transformer." IEEE Access 9 (2021): 123977–86. http://dx.doi.org/10.1109/access.2021.3109102.

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Sun, Che, Chenrui Shi, Yunde Jia, and Yuwei Wu. "Learning Event-Relevant Factors for Video Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 2384–92. http://dx.doi.org/10.1609/aaai.v37i2.25334.

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Most video anomaly detection methods discriminate events that deviate from normal patterns as anomalies. However, these methods are prone to interferences from event-irrelevant factors, such as background textures and object scale variations, incurring an increased false detection rate. In this paper, we propose to explicitly learn event-relevant factors to eliminate the interferences from event-irrelevant factors on anomaly predictions. To this end, we introduce a causal generative model to separate the event-relevant factors and event-irrelevant ones in videos, and learn the prototypes of event-relevant factors in a memory augmentation module. We design a causal objective function to optimize the causal generative model and develop a counterfactual learning strategy to guide anomaly predictions, which increases the influence of the event-relevant factors. The extensive experiments show the effectiveness of our method for video anomaly detection.
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Li, Nannan, Xinyu Wu, Huiwen Guo, Dan Xu, Yongsheng Ou, and Yen-Lun Chen. "Anomaly Detection in Video Surveillance via Gaussian Process." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 06 (August 12, 2015): 1555011. http://dx.doi.org/10.1142/s0218001415550113.

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In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze different anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce false detections caused by crowd occlusion, we utilize supplement information from previous frames to assist in anomaly detection for current frame. In addition, we address the problem of hyperparameter tuning and discuss the method of efficient calculation to reduce computation overhead. The approach is verified on published anomaly detection datasets and compared with other existing methods. The experiment results demonstrate that it can detect various anomalies efficiently and accurately.
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Sun, Li, Zhiguo Wang, Yujin Zhang, and Guijin Wang. "A Feature-Trajectory-Smoothed High-Speed Model for Video Anomaly Detection." Sensors 23, no. 3 (February 2, 2023): 1612. http://dx.doi.org/10.3390/s23031612.

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High-speed detection of abnormal frames in surveillance videos is essential for security. This paper proposes a new video anomaly–detection model, namely, feature trajectory–smoothed long short-term memory (FTS-LSTM). This model trains an LSTM autoencoder network to generate future frames on normal video streams, and uses the FTS detector and generation error (GE) detector to detect anomalies on testing video streams. FTS loss is a new indicator in the anomaly–detection area. In the training stage, the model applies a feature trajectory smoothness (FTS) loss to constrain the LSTM layer. This loss enables the LSTM layer to learn the temporal regularity of video streams more precisely. In the detection stage, the model utilizes the FTS loss and the GE loss as two detectors to detect anomalies. By cascading the FTS detector and the GE detector to detect anomalies, the model achieves a high speed and competitive anomaly-detection performance on multiple datasets.
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Bansod, Suprit, and Abhijeet Nandedkar. "Transfer learning for video anomaly detection." Journal of Intelligent & Fuzzy Systems 36, no. 3 (March 26, 2019): 1967–75. http://dx.doi.org/10.3233/jifs-169908.

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Yang, Fan, Zhiwen Yu, Liming Chen, Jiaxi Gu, Qingyang Li, and Bin Guo. "Human-Machine Cooperative Video Anomaly Detection." Proceedings of the ACM on Human-Computer Interaction 4, CSCW3 (January 5, 2021): 1–18. http://dx.doi.org/10.1145/3434183.

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Tao Xiang and Shaogang Gong. "Video Behavior Profiling for Anomaly Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 5 (May 2008): 893–908. http://dx.doi.org/10.1109/tpami.2007.70731.

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12

Varghese, Emmanu, Jaison Mulerikkal, and Amitha Mathew. "Video Anomaly Detection in Confined Areas." Procedia Computer Science 115 (2017): 448–59. http://dx.doi.org/10.1016/j.procs.2017.09.104.

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Islam, Muhammad, Abdulsalam S. Dukyil, Saleh Alyahya, and Shabana Habib. "An IoT Enable Anomaly Detection System for Smart City Surveillance." Sensors 23, no. 4 (February 20, 2023): 2358. http://dx.doi.org/10.3390/s23042358.

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Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems that rely on human involvement for anomaly detection, which is tedious and inaccurate. Due to the diverse nature of anomalous events and their complexity, it is however, very challenging to detect them automatically in a real-world scenario. By using Artificial Intelligence of Things (AIoT), this research work presents an efficient and robust framework for detecting anomalies in surveillance large video data. A hybrid model integrating 2D-CNN and ESN are proposed in this research study for smart surveillance, which is an important application of AIoT. The CNN is used as feature extractor from input videos which are then inputted to autoencoder for feature refinement followed by ESN for sequence learning and anomalous events detection. The proposed model is lightweight and implemented over edge devices to ensure their capability and applicability over AIoT environments in a smart city. The proposed model significantly enhanced performance using challenging surveillance datasets compared to other methods.
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Chen, Junzhou, Jiancheng Wang, Jiajun Pu, and Ronghui Zhang. "A Three-Stage Anomaly Detection Framework for Traffic Videos." Journal of Advanced Transportation 2022 (July 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/9463559.

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As reported by the United Nations in 2021, road accidents cause 1.3 million deaths and 50 million injuries worldwide each year. Detecting traffic anomalies timely and taking immediate emergency response and rescue measures are essential to reduce casualties, economic losses, and traffic congestion. This paper proposed a three-stage method for video-based traffic anomaly detection. In the first stage, the ViVit network is employed as a feature extractor to capture the spatiotemporal features from the input video. In the second stage, the class and patch tokens are fed separately to the segment-level and video-level traffic anomaly detectors. In the third stage, we finished the construction of the entire composite traffic anomaly detection framework by fusing outputs of two traffic anomaly detectors above with different granularity. Experimental evaluation demonstrates that the proposed method outperforms the SOTA method with 2.07% AUC on the TAD testing overall set and 1.43% AUC on the TAD testing anomaly subset. This work provides a new reference for traffic anomaly detection research.
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Shin, Wonsup, Seok-Jun Bu, and Sung-Bae Cho. "3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance." International Journal of Neural Systems 30, no. 06 (May 28, 2020): 2050034. http://dx.doi.org/10.1142/s0129065720500343.

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As the surveillance devices proliferate, various machine learning approaches for video anomaly detection have been attempted. We propose a hybrid deep learning model composed of a video feature extractor trained by generative adversarial network with deficient anomaly data and an anomaly detector boosted by transferring the extractor. Experiments with UCSD pedestrian dataset show that it achieves 94.4% recall and 86.4% precision, which is the competitive performance in video anomaly detection.
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Qi, Xiaosha, Zesheng Hu, and Genlin Ji. "Improved Video Anomaly Detection with Dual Generators and Channel Attention." Applied Sciences 13, no. 4 (February 10, 2023): 2284. http://dx.doi.org/10.3390/app13042284.

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Video anomaly detection is a crucial aspect of understanding surveillance videos in real-world scenarios and has been gaining attention in the computer vision community. However, a significant challenge is that the training data only include normal events, making it difficult for models to learn abnormal patterns. To address this issue, we propose a novel dual-generator generative adversarial network method that improves the model’s ability to detect unknown anomalies by learning the anomaly distribution in advance. Our approach consists of a noise generator and a reconstruction generator, where the former focuses on generating pseudo-anomaly frames and the latter aims to comprehensively learn the distribution of normal video frames. Furthermore, the integration of a second-order channel attention module enhances the learning capacity of the model. Experiments on two popular datasets demonstrate the superiority of our proposed method and show that it can effectively detect abnormal frames after learning the pseudo-anomaly distribution in advance.
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Song, Yide. "Weakly-Supervised and Unsupervised Video Anomaly Detection." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 160–70. http://dx.doi.org/10.54097/hset.v12i.1444.

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As surveillance technology is continuously improving, an ever-increasing number of cameras are being deployed everywhere. Relying on manual detection of anomalies through cameras may be unreliable and untimely. Therefore, the application of deep learning in video anomaly detection is being extensively studied. Anomaly Detection (AD) refers to identifying events that deviate from the desired actions. This article discusses representative unsupervised and weakly-supervised learning methods applied to various data types. In these machine learning methods, Generative Adversarial Network, Auto Encoder, Recurrent Neural Network, etc. are broadly adopted for AD. Some renowned and new datasets are reviewed. Furthermore, we also proposed several future directions of research in video anomaly detection.
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18

Xia, Xiangli, and Yang Gao. "Video Abnormal Event Detection Based on One-Class Neural Network." Computational Intelligence and Neuroscience 2021 (September 28, 2021): 1–7. http://dx.doi.org/10.1155/2021/1955116.

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Video abnormal event detection is a challenging problem in pattern recognition field. Existing methods usually design the two steps of video feature extraction and anomaly detection model establishment independently, which leads to the failure to achieve the optimal result. As a remedy, a method based on one-class neural network (ONN) is designed for video anomaly detection. The proposed method combines the layer-by-layer data representation capabilities of the autoencoder and good classification capabilities of ONN. The features of the hidden layer are constructed for the specific task of anomaly detection, thereby obtaining a hyperplane to separate all normal samples from abnormal ones. Experimental results show that the proposed method achieves 94.9% frame-level AUC and 94.5% frame-level AUC on the PED1 subset and PED2 subset from the USCD dataset, respectively. In addition, it achieves 80 correct event detections on the Subway dataset. The results confirm the wide applicability and good performance of the proposed method in industrial and urban environments.
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Chang, Yunpeng, Zhigang Tu, Wei Xie, Bin Luo, Shifu Zhang, Haigang Sui, and Junsong Yuan. "Video anomaly detection with spatio-temporal dissociation." Pattern Recognition 122 (February 2022): 108213. http://dx.doi.org/10.1016/j.patcog.2021.108213.

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20

Li, Zhaoyan, Yaoshun Li, and Zhisheng Gao. "Spatiotemporal Representation Learning for Video Anomaly Detection." IEEE Access 8 (2020): 25531–42. http://dx.doi.org/10.1109/access.2020.2970497.

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Xuan Mo, Vishal Monga, Raja Bala, and Zhigang Fan. "Adaptive Sparse Representations for Video Anomaly Detection." IEEE Transactions on Circuits and Systems for Video Technology 24, no. 4 (April 2014): 631–45. http://dx.doi.org/10.1109/tcsvt.2013.2280061.

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Li, H., A. Achim, and D. Bull. "Unsupervised video anomaly detection using feature clustering." IET Signal Processing 6, no. 5 (2012): 521. http://dx.doi.org/10.1049/iet-spr.2011.0074.

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Zhang, Qianqian, Hongyang Wei, Jiaying Chen, Xusheng Du, and Jiong Yu. "Video Anomaly Detection Based on Attention Mechanism." Symmetry 15, no. 2 (February 16, 2023): 528. http://dx.doi.org/10.3390/sym15020528.

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Camera surveillance is widely used in residential areas, highways, schools and other public places. The monitoring and scanning of sudden abnormal events depend on humans. Human anomaly monitoring not only consumes a lot of manpower and time but also has a large error in anomaly detection. Video anomaly detection based on AE (Auto-Encoder) is currently the dominant research approach. The model has a highly symmetrical network structure in the encoding and decoding stages. The model is trained by learning standard video sequences, and the anomalous events are later determined in terms of reconstruction error and prediction error. However, in the case of limited computing power, the complex model will greatly reduce the detection efficiency, and unnecessary background information will seriously affect the detection accuracy of the model. This paper uses the AE loaded with dynamic prototype units as the basic model. We introduce an attention mechanism to improve the feature representation ability of the model. Deep separable convolution operation can effectively reduce the number of model parameters and complexity. Finally, we conducted experiments on three publicly available datasets of real scenarios (UCSD Ped1, UCSD Ped2 and CUHK Avenue). The experimental results show that compared with the baseline model, the accuracy of our model improved by 1.9%, 1.4% and 6.6%, respectively, across the three datasets. Compared with many popular models, the validity of our model in anomaly detection is verified.
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Vu, Hung, Tu Dinh Nguyen, Trung Le, Wei Luo, and Dinh Phung. "Robust Anomaly Detection in Videos Using Multilevel Representations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5216–23. http://dx.doi.org/10.1609/aaai.v33i01.33015216.

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Detecting anomalies in surveillance videos has long been an important but unsolved problem. In particular, many existing solutions are overly sensitive to (often ephemeral) visual artifacts in the raw video data, resulting in false positives and fragmented detection regions. To overcome such sensitivity and to capture true anomalies with semantic significance, one natural idea is to seek validation from abstract representations of the videos. This paper introduces a framework of robust anomaly detection using multilevel representations of both intensity and motion data. The framework consists of three main components: 1) representation learning using Denoising Autoencoders, 2) level-wise representation generation using Conditional Generative Adversarial Networks, and 3) consolidating anomalous regions detected at each representation level. Our proposed multilevel detector shows a significant improvement in pixel-level Equal Error Rate, namely 11.35%, 12.32% and 4.31% improvement in UCSD Ped 1, UCSD Ped 2 and Avenue datasets respectively. In addition, the model allowed us to detect mislabeled anomalies in the UCDS Ped 1.
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Hu, Jingtao, En Zhu, Siqi Wang, Xinwang Liu, Xifeng Guo, and Jianping Yin. "An Efficient and Robust Unsupervised Anomaly Detection Method Using Ensemble Random Projection in Surveillance Videos." Sensors 19, no. 19 (September 24, 2019): 4145. http://dx.doi.org/10.3390/s19194145.

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Video anomaly detection is widely applied in modern society, which is achieved by sensors such as surveillance cameras. This paper learns anomalies by exploiting videos under the fully unsupervised setting. To avoid massive computation caused by back-prorogation in existing methods, we propose a novel efficient three-stage unsupervised anomaly detection method. In the first stage, we adopt random projection instead of autoencoder or its variants in previous works. Then we formulate the optimization goal as a least-square regression problem which has a closed-form solution, leading to less computational cost. The discriminative reconstruction losses of normal and abnormal events encourage us to roughly estimate normality that can be further sifted in the second stage with one-class support vector machine. In the third stage, to eliminate the instability caused by random parameter initializations, ensemble technology is performed to combine multiple anomaly detectors’ scores. To the best of our knowledge, it is the first time that unsupervised ensemble technology is introduced to video anomaly detection research. As demonstrated by the experimental results on several video anomaly detection benchmark datasets, our algorithm robustly surpasses the recent unsupervised methods and performs even better than some supervised approaches. In addition, we achieve comparable performance contrast with the state-of-the-art unsupervised method with much less running time, indicating the effectiveness, efficiency, and robustness of our proposed approach.
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Naik, Anuja Jana, and Gopalakrishna Madigondanahalli Thimmaiah. "Detection and Localization of Anamoly in Videos Using Fruit Fly Optimization-Based Self Organized Maps." International Journal of Safety and Security Engineering 11, no. 6 (December 28, 2021): 703–11. http://dx.doi.org/10.18280/ijsse.110611.

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Detection of anomalies in crowded videos has become an eminent field of research in the community of computer vision. Variation in scene normalcy obtained by training labeled and unlabelled data is identified as Anomaly by diverse traditional approaches. There is no hardcore isolation among anomalous and non-anomalous events; it can mislead the learning process. This paper plans to develop an efficient model for anomaly detection in crowd videos. The video frames are generated for accomplishing that, and feature extraction is adopted. The feature extraction methods like Histogram of Oriented Gradients (HOG) and Local Gradient Pattern (LGP) are used. Further, the meta-heuristic training-based Self Organized Map (SOM) is used for detection and localization. The training of SOM is enhanced by the Fruit Fly Optimization Algorithm (FOA). Moreover, the flow of objects and their directions are determined for localizing the anomaly objects in the detected videos. Finally, comparing the state-of-the-art techniques shows that the proposed model outperforms most competing models on the standard video surveillance dataset.
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Berroukham, Abdelhafid, Khalid Housni, Mohammed Lahraichi, and Idir Boulfrifi. "Deep learning-based methods for anomaly detection in video surveillance: a review." Bulletin of Electrical Engineering and Informatics 12, no. 1 (February 1, 2023): 314–27. http://dx.doi.org/10.11591/eei.v12i1.3944.

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Detecting anomalous events in videos is one of the most popular computer vision topics. It is considered a challenging task in video analysis due to its definition, which is subjective or context-dependent. Various approaches have been proposed to address the anomaly detection problems. These approaches vary from hand-crafted to deep learning. Many researchers have gone into determining the best approach for effectively detecting anomalies in video streams while maintaining a low false alarm rate. The results proved that approaches based on deep learning offer very interesting results in this field. In this paper, we review a family of video anomaly detection approaches based on deep learning techniques, which are compared in terms of their algorithms and models. Moreover, we have grouped state-of-the-art methods into different categories based on the approach adopted to differentiate between normal and abnormal events, and the underlying assumptions. Furthermore, we also present publicly available datasets and evaluation metrics used in existing works. Finally, we provide a comparison and discussion on the results of various approaches according to different datasets. This paper can be a good starting point for such researchers to understand this field and review existing work related to this topic.
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Khan, Sardar Waqar, Qasim Hafeez, Muhammad Irfan Khalid, Roobaea Alroobaea, Saddam Hussain, Jawaid Iqbal, Jasem Almotiri, and Syed Sajid Ullah. "Anomaly Detection in Traffic Surveillance Videos Using Deep Learning." Sensors 22, no. 17 (August 31, 2022): 6563. http://dx.doi.org/10.3390/s22176563.

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In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.
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Ma, Zhen, José J. M. Machado, and João Manuel R. S. Tavares. "Weakly Supervised Video Anomaly Detection Based on 3D Convolution and LSTM." Sensors 21, no. 22 (November 12, 2021): 7508. http://dx.doi.org/10.3390/s21227508.

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Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness.
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Bian, Yihan, and Xinchen Tang. "Abnormal Detection in Big Data Video with an Improved Autoencoder." Computational Intelligence and Neuroscience 2021 (December 8, 2021): 1–6. http://dx.doi.org/10.1155/2021/9861533.

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With the rapid growth of video surveillance data, there is an increasing demand for big data automatic anomaly detection of large-scale video data. The detection methods using reconstruction errors based on deep autoencoders have been widely discussed. However, sometimes the autoencoder could reconstruct the anomaly well and lead to missing detections. In order to solve this problem, this paper uses a memory module to enhance the autoencoder, which is called the memory-augmented autoencoder (Memory AE) method. Given the input, Memory AE first obtains the code from the encoder and then uses it as a query to retrieve the most relevant memory items for reconstruction. In the training phase, the memory content is updated and encouraged to represent prototype elements of normal data. In the test phase, the learned memory elements are fixed, and reconstruction is obtained from several selected memory records of normal data. So, the reconstruction will tend to be close to normal samples. Therefore, the reconstruction of abnormal errors will be strengthened for abnormal detection. The experimental results on two public video anomaly detection datasets, i.e., Avenue dataset and ShanghaiTech dataset, prove the effectiveness of the proposed method.
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Montenegro, Juan, and Yeojin Chung. "Semi-supervised generative adversarial networks for anomaly detection." SHS Web of Conferences 132 (2022): 01016. http://dx.doi.org/10.1051/shsconf/202213201016.

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Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos. Additionally, we intend to tackle one of the most recurring difficulties of anomaly detection: illumination. For this, we propose a light augmentation algorithm based on gamma correction to help the semi-supervised generative adversarial networks on its classification task. The proposed process performs slightly better than other proposed models.
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Xiao, Tan, Chao Zhang, and Hongbin Zha. "Anomaly Detection via Midlevel Visual Attributes." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/343869.

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Automatically discovering anomalous events and objects from surveillance videos plays an important role in real-world application and has attracted considerable attention in computer vision community. However it is still a challenging issue. In this paper, a novel approach for automatic anomaly detection is proposed. Our approach is highly efficient; thus it can perform real-time detection. Furthermore, it can also handle multiscale detection and can cope with spatial and temporal anomalies. Specifically, local features capturing both appearance and motion characteristics of videos are extracted from spatiotemporal video volume (STV). To bridge the large semantic gap between low-level visual feature and high-level event, we use the middle-level visual attributes as the intermediary. And these three-level framework is modeled as an extreme learning machine (ELM). We propose to use the spatiotemporal pyramid (STP) to capture the spatial and temporal continuity of an anomalous even, enabling our approach to cope with multiscale and complicated events. Furthermore, we propose a method to efficiently update the ELM; thus our approach is self-adaptive to background change which often occurs in real-world application. Experiments on several datasets are carried out and the superior performance of our approach compared to the state-of-the-art approaches verifies its effectiveness.
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33

Sahu, Swapna Kumari, and Dr M. Jayanthi Rao. "A Spatial-Temporal based Next Frame Prediction and Unsupervised Classification of Video Anomalies in Real Time Estimation." International Journal of Engineering and Advanced Technology 11, no. 1 (October 30, 2021): 120–24. http://dx.doi.org/10.35940/ijeat.a3161.1011121.

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Anomaly detection is an area of video analysis has a great importance in automated surveillance. Although it has been extensively studied, there has been little work started using CNN networks. Hence, in this thesis we presented a novel approach for learning motion features and modeling normal Spatio-temporal dynamics for anomaly detection. In our technique, we capture variations in scale of the patterns of motion in an image object by using optical flow dense estimation technique and train our auto encoder model using convolution long short term memories (ConvLSTM2D) as we are processing video frames and we predict the anomaly in real time using Euclidean distance between the generated and the ground truth frame and we achieved a real time accuracy of nearly 98% for the youtube videos which are not used for either testing or training. Error between the network’s output and the target output is used to classify a video volume as normal or abnormal. In addition to the use of reconstruction error, we also use prediction error for anomaly detection. The prediction models show comparable performance with state of the art methods. In comparison with the proposed method, performance is improved in one dataset. Moreover, running time is significantly faster.
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34

Kim, Jaehyun, Seongwook Yoon, Taehyeon Choi, and Sanghoon Sull. "Unsupervised Video Anomaly Detection Based on Similarity with Predefined Text Descriptions." Sensors 23, no. 14 (July 9, 2023): 6256. http://dx.doi.org/10.3390/s23146256.

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Research on video anomaly detection has mainly been based on video data. However, many real-world cases involve users who can conceive potential normal and abnormal situations within the anomaly detection domain. This domain knowledge can be conveniently expressed as text descriptions, such as “walking” or “people fighting”, which can be easily obtained, customized for specific applications, and applied to unseen abnormal videos not included in the training dataset. We explore the potential of using these text descriptions with unlabeled video datasets. We use large language models to obtain text descriptions and leverage them to detect abnormal frames by calculating the cosine similarity between the input frame and text descriptions using the CLIP visual language model. To enhance the performance, we refined the CLIP-derived cosine similarity using an unlabeled dataset and the proposed text-conditional similarity, which is a similarity measure between two vectors based on additional learnable parameters and a triplet loss. The proposed method has a simple training and inference process that avoids the computationally intensive analyses of optical flow or multiple frames. The experimental results demonstrate that the proposed method outperforms unsupervised methods by showing 8% and 13% better AUC scores for the ShanghaiTech and UCFcrime datasets, respectively. Although the proposed method shows −6% and −5% than weakly supervised methods for those datasets, in abnormal videos, the proposed method shows 17% and 5% better AUC scores, which means that the proposed method shows comparable results with weakly supervised methods that require resource-intensive dataset labeling. These outcomes validate the potential of using text descriptions in unsupervised video anomaly detection.
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Wang, Bokun, and Caiqian Yang. "Video Anomaly Detection Based on Convolutional Recurrent AutoEncoder." Sensors 22, no. 12 (June 20, 2022): 4647. http://dx.doi.org/10.3390/s22124647.

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As an essential task in computer vision, video anomaly detection technology is used in video surveillance, scene understanding, road traffic analysis and other fields. However, the definition of anomaly, scene change and complex background present great challenges for video anomaly detection tasks. The insight that motivates this study is that the reconstruction error for normal samples would be lower since they are closer to the training data, while the anomalies could not be reconstructed well. In this paper, we proposed a Convolutional Recurrent AutoEncoder (CR-AE), which combines an attention-based Convolutional Long–Short-Term Memory (ConvLSTM) network and a Convolutional AutoEncoder. The ConvLSTM network and the Convolutional AutoEncoder could capture the irregularity of the temporal pattern and spatial irregularity, respectively. The attention mechanism was used to obtain the current output characteristics from the hidden state of each Covn-LSTM layer. Then, a convolutional decoder was utilized to reconstruct the input video clip and the testing video clip with higher reconstruction error, which were further judged to be anomalies. The proposed method was tested on two popular benchmarks (UCSD ped2 Dataset and Avenue Dataset), and the experimental results demonstrated that CR-AE achieved 95.6% and 73.1% frame-level AUC on two public datasets, respectively.
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36

Li, Shuo, Fang Liu, and Licheng Jiao. "Self-Training Multi-Sequence Learning with Transformer for Weakly Supervised Video Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 1395–403. http://dx.doi.org/10.1609/aaai.v36i2.20028.

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Weakly supervised Video Anomaly Detection (VAD) using Multi-Instance Learning (MIL) is usually based on the fact that the anomaly score of an abnormal snippet is higher than that of a normal snippet. In the beginning of training, due to the limited accuracy of the model, it is easy to select the wrong abnormal snippet. In order to reduce the probability of selection errors, we first propose a Multi-Sequence Learning (MSL) method and a hinge-based MSL ranking loss that uses a sequence composed of multiple snippets as an optimization unit. We then design a Transformer-based MSL network to learn both video-level anomaly probability and snippet-level anomaly scores. In the inference stage, we propose to use the video-level anomaly probability to suppress the fluctuation of snippet-level anomaly scores. Finally, since VAD needs to predict the snippet-level anomaly scores, by gradually reducing the length of selected sequence, we propose a self-training strategy to gradually refine the anomaly scores. Experimental results show that our method achieves significant improvements on ShanghaiTech, UCF-Crime, and XD-Violence.
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37

Hao, Yi, Jie Li, Nannan Wang, Xiaoyu Wang, and Xinbo Gao. "Spatiotemporal consistency-enhanced network for video anomaly detection." Pattern Recognition 121 (January 2022): 108232. http://dx.doi.org/10.1016/j.patcog.2021.108232.

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38

Pereira, Silas Santiago L., and José E. B. Maia. "Anomaly Detection in Surveillance Video of Natural Environment." International Journal of Computer Applications 183, no. 1 (May 19, 2021): 1–7. http://dx.doi.org/10.5120/ijca2021921288.

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Li, Yuanyuan, Yiheng Cai, Jiaqi Liu, Shinan Lang, and Xinfeng Zhang. "Spatio-Temporal Unity Networking for Video Anomaly Detection." IEEE Access 7 (2019): 172425–32. http://dx.doi.org/10.1109/access.2019.2954540.

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40

Zhou, Joey Tianyi, Jiawei Du, Hongyuan Zhu, Xi Peng, Yong Liu, and Rick Siow Mong Goh. "AnomalyNet: An Anomaly Detection Network for Video Surveillance." IEEE Transactions on Information Forensics and Security 14, no. 10 (October 2019): 2537–50. http://dx.doi.org/10.1109/tifs.2019.2900907.

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41

Biswas, Sovan, and R. Venkatesh Babu. "Anomaly detection in compressed H.264/AVC video." Multimedia Tools and Applications 74, no. 24 (August 28, 2014): 11099–115. http://dx.doi.org/10.1007/s11042-014-2219-4.

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42

Feng, Jiangfan, Dini Wang, and Li Zhang. "Crowd Anomaly Detection via Spatial Constraints and Meaningful Perturbation." ISPRS International Journal of Geo-Information 11, no. 3 (March 18, 2022): 205. http://dx.doi.org/10.3390/ijgi11030205.

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Crowd anomaly detection is a practical and challenging problem to computer vision and VideoGIS due to abnormal events’ rare and diverse nature. Consequently, traditional methods rely on low-level reconstruction in a single image space, easily affected by unimportant pixels or sudden variations. In addition, real-time detection for crowd anomaly detection is challenging, and localization of anomalies requires other supervision. We present a new detection approach to learn spatiotemporal features with the spatial constraints of a still dynamic image. First, a lightweight spatiotemporal autoencoder has been proposed, capable of real-time image reconstruction. Second, we offer a dynamic network to obtain a compact representation of video frames in motion, reducing false-positive anomaly alerts by spatial constraints. In addition, we adopt the perturbation visual interpretation method for anomaly visualization and localization to improve the credibility of the results. In experiments, our results provide competitive performance across various scenarios. Besides, our approach can process 52.9–63.4 fps in anomaly detection, making it practical for crowd anomaly detection in video surveillance.
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43

Liu, Ting, Chengqing Zhang, Xiaodong Niu, and Liming Wang. "Spatio-temporal prediction and reconstruction network for video anomaly detection." PLOS ONE 17, no. 5 (May 26, 2022): e0265564. http://dx.doi.org/10.1371/journal.pone.0265564.

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The existing anomaly detection methods can be divided into two popular models based on reconstruction or future frame prediction. Due to the strong learning capacity, reconstruction approach can hardly generate significant reconstruction errors for anomalies, whereas future frame prediction approach is sensitive to noise in complicated scenarios. Therefore, a solution has been proposed by balancing the merits and demerits of the two models. However, most methods relied on single-scale information to capture spatial features and lacked temporal continuity between the video frames, affecting anomaly detection accuracy. Thus, we propose a novel method to improve anomaly detection performance. Because of the objects of various scales in each video, we select different receptive fields to extract comprehensive spatial features by the hybrid dilated convolution (HDC) module. Meanwhile, the deeper bidirectional convolutional long short-term memory (DB-ConvLSTM) module can remember the temporal information between the consecutive frames. Experiments prove that our method can detect abnormalities in various video scenes more accurately than the state-of-the-art methods in the anomaly-detection task.
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44

Liu, Ting, Chengqing Zhang, and Liming Wang. "Integrated Multiscale Appearance Features and Motion Information Prediction Network for Anomaly Detection." Computational Intelligence and Neuroscience 2021 (October 20, 2021): 1–13. http://dx.doi.org/10.1155/2021/6789956.

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The rise of video-prediction algorithms has largely promoted the development of anomaly detection in video surveillance for smart cities and public security. However, most current methods relied on single-scale information to extract appearance (spatial) features and lacked motion (temporal) continuity between video frames. This can cause a loss of partial spatiotemporal information that has great potential to predict future frames, affecting the accuracy of abnormality detection. Thus, we propose a novel prediction network to improve the performance of anomaly detection. Due to the objects of various scales in each video, we use different receptive fields to extract detailed appearance features by the hybrid dilated convolution (HDC) module. Meanwhile, the deeper bidirectional convolutional long short-term memory (DB-ConvLSTM) module can remember the motion information between consecutive frames. Furthermore, we use RGB difference loss to replace optical flow loss as temporal constraint, which greatly reduces the time for optical flow extraction. Compared with the state-of-the-art methods in the anomaly-detection task, experiments prove that our method can more accurately detect abnormalities in various video surveillance scenes.
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45

Ananthakrishnan, Balasundaram, V. Padmaja, Sruthi Nayagi, and Vijay M. "Deep Neural Network based Anomaly Detection for Real Time Video Surveillance." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (April 30, 2022): 54–64. http://dx.doi.org/10.17762/ijritcc.v10i4.5534.

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One of the main concerns across all kinds of domains has always been security. With the crime rates increasing every year the need to control has become crucial. Among the various methods present to monitor crime or any anomalous behavior is through video surveillance. Nowadays security cameras capture incidents in almost all public and private place if desired. Even though we have abundance of data in the form of videos they need to be analyzed manually. This results in long hours of manual labour and even small human discrepancies may have huge consequences negatively. For this purpose, a Convolution Neural Network (CNN) based model is built to detect any form of abnormal activities or anomalies in the video footages. This model converts the input video into frames and detects the anomalous frames. To increase the efficiency of the model, the data is de-noised with Gaussian blur feature. The avenue dataset is used in this work to detect and predict various kinds of anomalies. The performance of the model is measured using classification accuracy and the results are reported.
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Kavimandan, Pranoti Shrikant, Rajiv Kapoor, and Kalpana Yadav. "Graph based anomaly detection in human action video sequence." Journal of Electrical Engineering 73, no. 5 (September 1, 2022): 318–24. http://dx.doi.org/10.2478/jee-2022-0042.

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Abstract In our paper, we have proposed to use graphs to detect anomaly in human action video. Although the detection of anomaly is a widely researched topic, but very few researchers have detected anomaly in action video using graphs. in our proposed method we have represented the smaller section (sub-section) of our input video as a graph where vertices of the graph are the space time interest points in the sub-section video and the association between the space time interest points exists. Thus, graphs for each sub section are created to look for a repeated substructure. We believe most of the actions inherently are repeated in nature. Thus, we have tried to capture the repetitive sub-structure of the action represented as a graph and used this repetitive sub-structure to compress the graph. If the compressed graph has few elements that have not been compressed, we suspect them as anomaly. But the threshold value takes care not to make the proposed method very much sensitive towards the few uncompressed elements. Our proposed method has been implemented on locally created “extended KTH” and “extended Weizmann” datasets with good accuracy score. The proposed method can also be extended for few more applications such as training athletes and taking elderly care.
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47

Mahmood, Sawsen Abdulhadi, Azal Monshed Abid, and Sadeq H. Lafta. "Anomaly event detection and localization of video clips using global and local outliers." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (November 1, 2021): 1063. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp1063-1073.

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The automatic detection of anomaly events in video sequence has become a critical issue and essential demand for the extensive deployment of computer vision systems such as video surveillance applications. An anomaly event in video can be denoted as outlier behavior within video frames which formulated by a deviation from the stable scene. In this paper, an anomaly event detection and localization method in video sequence is presented including multilevel strategy as temporal frames differences estimation, modelling of normal and abnormal behavior using regression model and finally density–based clustering to detect the outliers (abnormal event) at clips level. Hence, outlier score is obtained at the segment or clip level along video frames sequences. The proposed method seplits video frames into nonoverlapped clips using global outlier detection process. Afterward, at each clip, the local outliers are determined based on density of each clip. Extensive experiments were conducted upon two public video datasets which include dense and scattered outliers along video sequence. The experiments were performed on two common public datasets (Avenue) and University of California, San Diego (UCSD). The experimental results exhibited that the proposed method detects well outlier frames at clip level with lower computational complexity comparing to the state-of-the-art methods.
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48

Kotkar, Vijay A. "Scalable Anomaly Detection Framework in Video Surveillance Using Keyframe Extraction and Machine Learning Algorithms." Journal of Advanced Research in Dynamical and Control Systems 12, no. 7 (July 20, 2020): 395–408. http://dx.doi.org/10.5373/jardcs/v12i7/20202020.

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49

Xu, Ming, Xiaosheng Yu, Dongyue Chen, Chengdong Wu, and Yang Jiang. "An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders." Applied Sciences 9, no. 16 (August 14, 2019): 3337. http://dx.doi.org/10.3390/app9163337.

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Anomaly detection in crowded scenes is an important and challenging part of the intelligent video surveillance system. As the deep neural networks make success in feature representation, the features extracted by a deep neural network represent the appearance and motion patterns in different scenes more specifically, comparing with the hand-crafted features typically used in the traditional anomaly detection approaches. 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. Firstly, the raw frames series are provided as input to our variational auto-encoder without any preprocessing to learn the appearance and motion features of the receptive fields. Then, multiple Gaussian models are used to predict the anomaly scores of the corresponding receptive fields. Our proposed two-stage anomaly detection system is evaluated on the video surveillance dataset for a large scene, UCSD pedestrian datasets, and yields competitive performance compared with state-of-the-art methods.
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Li, Chaoneng, Guanwen Feng, Yiran Jia, Yunan Li, Jian Ji, and Qiguang Miao. "RETAD." International Journal of Data Warehousing and Mining 19, no. 2 (January 13, 2023): 1–14. http://dx.doi.org/10.4018/ijdwm.316460.

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Due to the rapid advancement of wireless sensor and location technologies, a large amount of mobile agent trajectory data has become available. Intelligent city systems and video surveillance all benefit from trajectory anomaly detection. The authors propose an unsupervised reconstruction error-based trajectory anomaly detection (RETAD) method for vehicles to address the issues of conventional anomaly detection, which include difficulty extracting features, are susceptible to overfitting, and have a poor anomaly detection effect. RETAD reconstructs the original vehicle trajectories through an autoencoder based on recurrent neural networks. The model obtains moving patterns of normal trajectories by eliminating the gap between the reconstruction results and the initial inputs. Anomalous trajectories are defined as those with a reconstruction error larger than anomaly threshold. Experimental results demonstrate that the effectiveness of RETAD in detecting anomalies is superior to traditional distance-based, density-based, and machine learning classification algorithms on multiple metrics.
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