Active Online Anomaly Detection Using Dirichlet Process Mixture Model and Gaussian Process Classification

Varadarajan, Jagannadan; Subramanian, Ramanathan; Ahuja, Narendra; Moulin, Pierre; Odobez, Jean-Marc

doi:10.1109/wacv.2017.74

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…So these methods are not suitable for effective real-time-time applications (J. Varadarajan et al, 2017). These issues are solved by using the Active learning approach in deep neural networks.…”

Section: Active Learning-based Modelsmentioning

confidence: 99%

Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model

Anoopa¹

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Around the world, the video surveillance system has gained wide acceptance and astonishing growth due to its broad applications. The surveillance system has become a paramount tool and benchmark for analyzing the harmony and safety of society. Anomaly detection and its associated applications play a key role in the integrity of the system. The aim of anomaly detection is to find rare and sparse occurrences of events from videos. Developing an accurate and time-efficient system i s s till remains c hallenging due to t he dynamic nature of a nomalies. The deep learning-based end-to-end system with full use of both spatial and temporal features from the input videos is proposed. The model combines the use of 2DCNN and Stacked LSTM to extract frame-level features through an anisotropic Gunnar Farneback Optical Flow algorithm. The system is evaluated on the benchmarked datasets namely UCSD Ped1 and UCSD Ped2, and it achieves an AUC of 95% and 94% respectively. The experimental results indicate that the proposed method is superior to state-of-the-art algorithms.

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Section: Active Learning-based Modelsmentioning

confidence: 99%

Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model

Anoopa¹

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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“…It relies on observation variables and tries to retrieve those observation variables which are already fixed at beginning. On the other hand, variants of GMM like Dirichlet based mixture GMM models [61] and adaptive GMM [133] do not just depend on observations and pertain to longer interaction between observations. To alleviate the shortcomings of GMM, a deep GMM is used in [ state as a region of the normal videos, based on the distributed character of video data, to describe the domain of the normal sample [72].…”

Section: Refmentioning

confidence: 99%

Abnormal Human Behavior Detection in Videos: A Review

Sun

Yuan

et al. 2021

ITC

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Modeling human behavior patterns for detecting the abnormal event has become an important domain in recentyears. A lot of efforts have been made for building smart video surveillance systems with the purpose ofscene analysis and making correct semantic inference from the video moving target. Current approaches havetransferred from rule-based to statistical-based methods with the need of efficient recognition of high-levelactivities. This paper presented not only an update expanding previous related researches, but also a study coveredthe behavior representation and the event modeling. Especially, we provided a new perspective for eventmodeling which divided the methods into the following subcategories: modeling normal event, predictionmodel, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluationschemes used for abnormal behavior detection in intelligent video surveillance. More researches will promotethe development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It isobviously encouraged and dictated by applications of supervising and monitoring in private and public space.The main purpose of this paper is to widely recognize recent available methods and represent the literature ina way of that brings key challenges into notice.

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“…[60] and, Blei and Frazier [5] propose variational inference based variants that address complexity challenges. Additionally, there exists exemplary work that has explored DPMM for the task of anomaly detection [15,23,26,49,55,59] that identify anomalies post clustering in a non-streaming setting. But unlike existing work that are based on exchangeable DPMM models, we propose a non-exchangeable evolving model that studies the dependencies in the order of the observations to jointly study clusters and anomalies.…”

Section: Anomaly Detection Using Dpmmmentioning

confidence: 99%

Tracking clusters and anomalies in evolving data streams

Guggilam

Chandola

Patra

2021

Statistical Analysis

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Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicability of such methods, since most methods are sensitive to the choice of the threshold, and it is challenging to set optimal thresholds. The issue is exacerbated in a streaming scenario, where the optimal thresholds vary with time. We present a probabilistic framework to explicitly model the normal and anomalous behaviors and probabilistically reason about the data. An extreme value theory based formulation is proposed to model the anomalous behavior as the extremes of the normal behavior. As a specific instantiation, a joint nonparametric clustering and anomaly detection algorithm (INCAD) is proposed that models the normal behavior as a Dirichlet process mixture model. Results on a variety of datasets, including streaming data, show that the proposed method provides effective and simultaneous clustering and anomaly detection without requiring strong initialization and threshold parameters.

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Active Online Anomaly Detection Using Dirichlet Process Mixture Model and Gaussian Process Classification

Cited by 10 publications

References 28 publications

Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model

Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model

Abnormal Human Behavior Detection in Videos: A Review

Tracking clusters and anomalies in evolving data streams

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