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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "VIDEO ANOMALY DETECTION"

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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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Анотація:
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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Анотація:
(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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3

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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Анотація:
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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4

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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
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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Дисертації з теми "VIDEO ANOMALY DETECTION"

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Tran, Thi Minh Hanh. "Anomaly detection in video." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/22443/.

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Анотація:
Anomaly detection is an area of video analysis that has great importance in automated surveillance. Although it has been extensively studied, there has been little work on using deep convolutional neural networks to learn spatio-temporal feature representations. In this thesis we present novel approaches for learning motion features and modelling normal spatio-temporal dynamics for anomaly detection. The contributions are divided into two main chapters. The first introduces a method that uses a convolutional autoencoder to learn motion features from foreground optical flow patches. The autoencoder is coupled with a spatial sparsity constraint, known as Winner-Take-All, to learn shift-invariant and generic flow-features. This method solves the problem of using hand-crafted feature representations in state of the art methods. Moreover, to capture variations in scale of the patterns of motion as an object moves in depth through the scene,we also divide the image plane into regions and learn a separate normality model in each region. We compare the methods with state of the art approaches on two datasets and demonstrate improved performance. The second main chapter presents a end-to-end method that learns normal spatio-temporal dynamics from video volumes using a sequence-to-sequence encoder-decoder for prediction and reconstruction. This work is based on the intuition that the encoder-decoder learns to estimate normal sequences in a training set with low error, thus it estimates an abnormal sequence with high error. Error between the network's output and the target 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. We evaluate the second method on three datasets. The prediction models show comparable performance with state of the art methods. In comparison with the first proposed method, performance is improved in one dataset. Moreover, running time is significantly faster.
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Tziakos, Ioannis. "Subspace discovery for video anomaly detection." Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/387.

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Анотація:
In automated video surveillance anomaly detection is a challenging task. We address this task as a novelty detection problem where pattern description is limited and labelling information is available only for a small sample of normal instances. Classification under these conditions is prone to over-fitting. The contribution of this work is to propose a novel video abnormality detection method that does not need object detection and tracking. The method is based on subspace learning to discover a subspace where abnormality detection is easier to perform, without the need of detailed annotation and description of these patterns. The problem is formulated as one-class classification utilising a low dimensional subspace, where a novelty classifier is used to learn normal actions automatically and then to detect abnormal actions from low-level features extracted from a region of interest. The subspace is discovered (using both labelled and unlabelled data) by a locality preserving graph-based algorithm that utilises the Graph Laplacian of a specially designed parameter-less nearest neighbour graph. The methodology compares favourably with alternative subspace learning algorithms (both linear and non-linear) and direct one-class classification schemes commonly used for off-line abnormality detection in synthetic and real data. Based on these findings, the framework is extended to on-line abnormality detection in video sequences, utilising multiple independent detectors deployed over the image frame to learn the local normal patterns and infer abnormality for the complete scene. The method is compared with an alternative linear method to establish advantages and limitations in on-line abnormality detection scenarios. Analysis shows that the alternative approach is better suited for cases where the subspace learning is restricted on the labelled samples, while in the presence of additional unlabelled data the proposed approach using graph-based subspace learning is more appropriate.
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3

Leach, Michael Jeremy Vincent. "Automatic human behaviour anomaly detection in surveillance video." Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/3014.

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Анотація:
This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic human behaviour anomaly detection we address the challenge of interpreting behaviour within the context of the social and physical environment. We develop and evaluate a process for measuring social connectivity between individuals in a scene using motion and visual attention features. To do this we use mutual information and Euclidean distance to build a social similarity matrix which encodes the social connection strength between any two individuals. We de- velop a second contextual basis which acts by segmenting a surveillance environment into behaviourally homogeneous subregions which represent high tra c slow regions and queuing areas. We model the heterogeneous scene in homogeneous subgroups using both contextual elements. We bring the social contextual information, the scene context, the motion, and visual attention features together to demonstrate a novel human behaviour anomaly detection process which nds outlier behaviour from a short sequence of video. The method, Nearest Neighbour Ranked Outlier Clusters (NN-RCO), is based upon modelling behaviour as a time independent se- quence of behaviour events, can be trained in advance or set upon a single sequence. We nd that in a crowded scene the application of Mutual Information-based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in all the datasets we test upon. We additionally demonstrate that our work is applicable to other data domains. We demonstrate upon the Automatic Identi cation Signal data in the maritime domain. Our work is capable of identifying abnormal shipping behaviour using joint motion dependency as analogous for social connectivity, and similarly segmenting the shipping environment into homogeneous regions.
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Au, Carmen E. "Compression-based anomaly detection for video surveillance applications." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98598.

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Анотація:
In light of increased demands for security, we propose a unique approach to automated video surveillance using anomaly detection. The success of this approach is dependent on the ability of the system to ascertain the novelty of a given image acquired by a video camera. We adopt a compression-based similarity measure to determine similarity between images in a video sequence. Images that are sufficiently similar to the previously-seen images are discarded; conversely, images that are sufficiently dissimilar are stored for comparison with future incoming images.
The use of a compression-based technique inherently reduces the heavy computational and storage demands that other video surveillance applications typically have placed on the system. In order to further reduce the computational and storage load, the anomaly detection algorithm is applied to edges and people, which are image features that have been extracted from the images acquired by the camera.
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Laxhammar, Rikard. "Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications." Doctoral thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762.

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Анотація:
Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of anomalous trajectories in surveillance applications. The main results and contributions of the thesis are two-fold. Firstly, we propose and discuss a novel approach for anomaly detection, called conformal anomaly detection, which is based on conformal prediction (Vovk et al.). In particular, we propose two general algorithms for anomaly detection: the conformal anomaly detector (CAD) and the computationally more efficient inductive conformal anomaly detector (ICAD). A key property of conformal anomaly detection, in contrast to previous methods, is that it provides a well-founded approach for the tuning of the anomaly threshold that can be directly related to the expected or desired alarm rate. Secondly, we propose and analyse two parameter-light algorithms for unsupervised online learning and sequential detection of anomalous trajectories based on CAD and ICAD: the sequential Hausdorff nearest neighbours conformal anomaly detector (SHNN-CAD) and the sequential sub-trajectory local outlier inductive conformal anomaly detector (SSTLO-ICAD), which is more sensitive to local anomalous sub-trajectories. We implement the proposed algorithms and investigate their classification performance on a number of real and synthetic datasets from the video and maritime surveillance domains. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning on video trajectories. Moreover, we demonstrate that SSTLO-ICAD is able to accurately discriminate realistic anomalous vessel trajectories from normal background traffic.
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Isupova, Olga. "Machine learning methods for behaviour analysis and anomaly detection in video." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17771/.

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Анотація:
Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modeling and change point detection methodologies, respectively, are employed to achieve these objectives. The thesis starts with development of novel learning algorithms for a dynamic topic model. Topics extracted by the learning algorithms represent typical activities happening within an observed scene. These typical activities are used for likelihood computation. The likelihood serves as a normality measure in anomaly detection decision making. A novel anomaly localisation procedure is proposed. In the considered dynamic topic model a number of topics, i.e., typical activities, should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Conventional posterior inference algorithms require processing of the whole data through several passes. It is computationally intractable for massive or sequential data. Therefore, batch and online inference algorithms for the proposed model are developed. A novel normality measure is derived for decision making in anomaly detection. The latter part of the thesis considers behaviour analysis and anomaly detection within the change point detection methodology. A novel general framework for change point detection is introduced. Gaussian process time series data is considered and a change is defined as an alteration in hyperparameters of the Gaussian process prior. The problem is formulated in the context of statistical hypothesis testing and several tests suitable both for offline and online data processing and multiple change point detection are proposed. Theoretical properties of the proposed tests are derived based on the distribution of the test statistics.
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Spasic, Nemanja. "Anomaly Detection and Prediction of Human Actions in a Video Surveillance Environment." Thesis, University of Cape Town, 2007. http://pubs.cs.uct.ac.za/archive/00000449/.

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Анотація:
World wide focus has over the years been shifting towards security issues, not in least due to recent world wide terrorist activities. Several researchers have proposed state of the art surveillance systems to help with some of the security issues with varying success. Recent studies have suggested that the ability of these surveillance systems to learn common environmental behaviour patterns as wells as to detect and predict unusual, or anomalous, activities based on those learnt patterns are possible improvements to those systems. In addition, some of these surveillance systems are still run by human operators, who are prone to mistakes and may need some help from the surveillance systems themselves in detection of anomalous activities. This dissertation attempts to address these suggestions by combining the fields of Image Understanding and Artificial Intelligence, specifically Bayesian Networks, to develop a prototype video surveillance system that can learn common environmental behaviour patterns, thus being able to detect and predict anomalous activity in the environment based on those learnt patterns. In addition, this dissertation aims to show how the prototype system can adapt to these anomalous behaviours and integrate them into its common patterns over a prolonged occurrence period. The prototype video surveillance system showed good performance and ability to detect, predict and integrate anomalous activity in the evaluation tests that were performed using a volunteer in an experimental indoor environment. In addition, the prototype system performed quite well on the PETS 2002 dataset 1, which it was not designed for. The evaluation procedure used some of the evaluation metrics commonly used on the PETS datasets. Hence, the prototype system provides a good approach to anomaly detection and prediction using Bayesian Networks trained on common environmental activities.
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8

García, Ling Carlos. "Graphical Glitch Detection in Video Games Using CNNs." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273574.

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Анотація:
This work addresses the following research question: Can we detect videogame glitches using Convolutional Neural Networks? Focusing on the most common types of glitches, texture glitches (Stretched, Lower Resolution, Missing, and Placeholder). We first systematically generate a dataset with both images with texture glitches and normal samples.  To detect the faulty images we try both Classification and Semantic Segmentation approaches, with a clear focus on the former. The best setting in classification uses a ShuffleNetV2 architecture and obtains precisions of 80.0%, 64.3%, 99.2%, and 97.0% in the respective glitch classes Stretched, Lower Resolution, Missing, and Placeholder. All of this with a low false positive rate of 6.7%. To complement this study, we also discuss how the models extrapolate to different graphical environments, which are the main sources of confusion for the model, how to estimate the confidence of the network, and ways to interpret the internal behavior of the models.
Detta projekt svarar på följande forskningsfråga: Kan man använda Convolutional Neural Networks för att upptäcka felaktiga bilder i videospel? Vi fokuserar på de vanligast förekommande grafiska defekter i videospel, felaktiga textures (sträckt, lågupplöst, saknas och platshållare). Med hjälp av en systematisk process genererar vi data med både normala och felaktiga bilder. För att hitta defekter använder vi CNN via både Classification och Semantic Segmentation, med fokus på den första metoden. Den bäst presterande Classification-modellen baseras på ShuffleNetV2 och når 80.0%, 64.3%, 99.2% och 97.0% precision på respektive sträckt-, lågupplöst-, saknas- och platshållare-buggar. Detta medan endast 6.7% av negativa datapunkter felaktigt klassifieras som positiva. Denna undersökning ser även till hur modellen generaliserar till olika grafiska miljöer, vilka de primära orsakerna till förvirring hos modellen är, hur man kan bedöma säkerheten i nätverkets prediktion och hur man bättre kan förstå modellens interna struktur.
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Thornton, Daniel Richard. "Unusual-Object Detection in Color Video for Wilderness Search and Rescue." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2452.

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Анотація:
Aircraft-mounted cameras have potential to greatly increase the effectiveness of wilderness search and rescue efforts by collecting photographs or video of the search area. The more data that is collected, the more difficult it becomes to process it by visual inspection alone. This work presents a method for automatically detecting unusual objects in aerial video to assist people in locating signs of missing persons in wilderness areas. The detector presented here makes use of anomaly detection methods originally designed for hyperspectral imagery. Multiple anomaly detection methods are considered, implemented, and evaluated. These anomalies are then aggregated into spatiotemporal objects by using the video's inherent spatial and temporal redundancy. The results are therefore summarized into a list of unusual objects to enhance the search technician's video review interface. In the user study reported here, unusual objects found by the detector were overlaid on the video during review. This increased participants' ability to find relevant objects in a simulated search without significantly affecting the rate of false detection. Other effects and possible ways to improve the user interface are also discussed.
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10

Cheng, Guangchun. "Video Analytics with Spatio-Temporal Characteristics of Activities." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc799541/.

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Анотація:
As video capturing devices become more ubiquitous from surveillance cameras to smart phones, the demand of automated video analysis is increasing as never before. One obstacle in this process is to efficiently locate where a human operator’s attention should be, and another is to determine the specific types of activities or actions without ambiguity. It is the special interest of this dissertation to locate spatial and temporal regions of interest in videos and to develop a better action representation for video-based activity analysis. This dissertation follows the scheme of “locating then recognizing” activities of interest in videos, i.e., locations of potentially interesting activities are estimated before performing in-depth analysis. Theoretical properties of regions of interest in videos are first exploited, based on which a unifying framework is proposed to locate both spatial and temporal regions of interest with the same settings of parameters. The approach estimates the distribution of motion based on 3D structure tensors, and locates regions of interest according to persistent occurrences of low probability. Two contributions are further made to better represent the actions. The first is to construct a unifying model of spatio-temporal relationships between reusable mid-level actions which bridge low-level pixels and high-level activities. Dense trajectories are clustered to construct mid-level actionlets, and the temporal relationships between actionlets are modeled as Action Graphs based on Allen interval predicates. The second is an effort for a novel and efficient representation of action graphs based on a sparse coding framework. Action graphs are first represented using Laplacian matrices and then decomposed as a linear combination of primitive dictionary items following sparse coding scheme. The optimization is eventually formulated and solved as a determinant maximization problem, and 1-nearest neighbor is used for action classification. The experiments have shown better results than existing approaches for regions-of-interest detection and action recognition.
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Книги з теми "VIDEO ANOMALY DETECTION"

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Isupova, Olga. Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75508-3.

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Isupova, Olga. Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video. Springer, 2018.

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3

Isupova, Olga. Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video. Springer, 2019.

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Частини книг з теми "VIDEO ANOMALY DETECTION"

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Bala, Raja, and Vishal Monga. "Video Anomaly Detection." In Computer Vision and Imaging in Intelligent Transportation Systems, 227–56. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118971666.ch9.

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He, Xinyu, Fei Yuan, and Yi Zhu. "Drowning Detection Based on Video Anomaly Detection." In Lecture Notes in Computer Science, 700–711. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87361-5_57.

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Zhu, Yuansheng, Wentao Bao, and Qi Yu. "Towards Open Set Video Anomaly Detection." In Lecture Notes in Computer Science, 395–412. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19830-4_23.

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Zhu, Sijie, Chen Chen, and Waqas Sultani. "Video Anomaly Detection for Smart Surveillance." In Computer Vision, 1–8. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_845-1.

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Zhang, Yunzuo, Kaina Guo, Zhaoquan Cai, and Tianshan Fu. "Crowd Anomaly Detection in Surveillance Video." In Advances in Artificial Intelligence and Security, 3–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06761-7_1.

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Zhu, Sijie, Chen Chen, and Waqas Sultani. "Video Anomaly Detection for Smart Surveillance." In Computer Vision, 1315–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_845.

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Yadav, Divakar, Arti Jain, Saumya Asati, and Arun Kumar Yadav. "Video Anomaly Detection for Pedestrian Surveillance." In Computer Vision and Machine Intelligence, 489–500. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7867-8_39.

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Zhao, Chunyue, Beichen Li, Qing Wang, and Zhipeng Wang. "Video Anomaly Detection Based on Hierarchical Clustering." In Human Centered Computing, 547–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15127-0_55.

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Reiter, Wolfgang. "Video Anomaly Detection in Post-Procedural Use of Laparoscopic Videos." In Informatik aktuell, 101–6. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29267-6_22.

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Gnouma, Mariem, Ridha Ejbali, and Mourad Zaied. "Video Anomaly Detection and Localization in Crowded Scenes." In Advances in Intelligent Systems and Computing, 87–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20005-3_9.

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Тези доповідей конференцій з теми "VIDEO ANOMALY DETECTION"

1

Liu, Wen, Weixin Luo, Zhengxin Li, Peilin Zhao, and Shenghua Gao. "Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/419.

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Анотація:
Classical semi-supervised video anomaly detection assumes that only normal data are available in the training set because of the rare and unbounded nature of anomalies. It is obviously, however, these infrequently observed abnormal events can actually help with the detection of identical or similar abnormal events, a line of thinking that motivates us to study open-set supervised anomaly detection with only a few types of abnormal observed events and many normal events available. Under the assumption that normal events can be well predicted, we propose a Margin Learning Embedded Prediction (MLEP) framework. There are three features in MLEP- based open-set supervised video anomaly detection: i) we customize a video prediction framework that favors the prediction of normal events and distorts the prediction of abnormal events; ii) The margin learning framework learns a more compact normal data distribution and enlarges the margin between normal and abnormal events. Since abnormal events are unbounded, our framework consequently helps with the detection of abnormal events, even for anomalies that have never been previously observed. Therefore, our framework is suitable for the open-set supervised anomaly detection setting; iii) our framework can readily handle both frame-level and video-level anomaly annotations. Considering that video-level anomaly detection is more easily annotated in practice and that anomaly detection with a few anomalies is a more practical setting, our work thus pushes the application of anomaly detection towards real scenarios. Extensive experiments validate the effectiveness of our framework for anomaly detection.
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2

Cavas, Sumeyye, Muhammet Sebul Beratoglu, and Behcet Ugur Toreyin. "Anomaly Detection In Compressed Video." In 2021 29th Signal Processing and Communications Applications Conference (SIU). IEEE, 2021. http://dx.doi.org/10.1109/siu53274.2021.9478048.

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3

Doshi, Keval, and Yasin Yilmaz. "Towards Interpretable Video Anomaly Detection." In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. http://dx.doi.org/10.1109/wacv56688.2023.00268.

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4

Jing Wang and Zhijie Xu. "Crowd Anomaly Detection for Automated Video Surveillance." In 6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15). Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/ic.2015.0102.

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5

Wu, Jie, Wei Zhang, Guanbin Li, Wenhao Wu, Xiao Tan, Yingying Li, Errui Ding, and Liang Lin. "Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/162.

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Анотація:
In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video. Specifically, given an untrimmed video, WSSTAD aims to localize a spatio-temporal tube (i.e., a sequence of bounding boxes at consecutive times) that encloses the abnormal event, with only coarse video-level annotations as supervision during training. To address this challenging task, we propose a dual-branch network which takes as input the proposals with multi-granularities in both spatial-temporal domains. Each branch employs a relationship reasoning module to capture the correlation between tubes/videolets, which can provide rich contextual information and complex entity relationships for the concept learning of abnormal behaviors. Mutually-guided Progressive Refinement framework is set up to employ dual-path mutual guidance in a recurrent manner, iteratively sharing auxiliary supervision information across branches. It impels the learned concepts of each branch to serve as a guide for its counterpart, which progressively refines the corresponding branch and the whole framework. Furthermore, we contribute two datasets, i.e., ST-UCF-Crime and STRA, consisting of videos containing spatio-temporal abnormal annotations to serve as the benchmarks for WSSTAD. We conduct extensive qualitative and quantitative evaluations to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to handle this task.
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Lv, Hui, Chunyan Xu, and Zhen Cui. "Global Information Guided Video Anomaly Detection." In MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3416277.

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Jiang, Fan, Junsong Yuan, Sotirios A. Tsaftaris, and Aggelos K. Katsaggelos. "Video anomaly detection in spatiotemporal context." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5650993.

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Sun, Che, Yunde Jia, and Yuwei Wu. "Evidential Reasoning for Video Anomaly Detection." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548091.

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Au, C. E., S. Skaff, and J. J. Clark. "Anomaly Detection for Video Surveillance Applications." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.273.

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Meher, Chinmaya Kumar, Rashmiranjan Nayak, and Umesh Chandra Pati. "Video Anomaly Detection Using Variational Autoencoder." In 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC). IEEE, 2022. http://dx.doi.org/10.1109/isssc56467.2022.10051511.

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