An Overview of Anomaly Detection in Video Surveillance

Kaur, Phulpreet; Gangadharappa, M.; Gautam, Shalu

doi:10.1109/icacccn.2018.8748454

Cited by 4 publications

(1 citation statement)

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“…Overview of anomaly detection in video surveillance is discussed in [11]. The raw input data is given to event encoding for trajectory, motion labels, shape activity model and spatiotemporal model.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Event Detection in Pedestrian Pathway Using Garch Model and MLP Classifier

Pattnaik¹

2019

International Journal of Advances in Signal and Image Sciences

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In computer vision, one of the complex research areas is video surveillance. It is very important to monitor the abnormal events in public places. Due to the technical advances, the usage of cameras is increased for surveillance purpose. As human operators are employed for the observation, their visual attention is reduced after long periods. Hence, an automated Abnormal Event Detection (AED) technique is designed in this study. It uses Generalized Autoregressive Conditional Heteroscedasticity (GARCH) which is a statistical model to model the events occurs in the pedestrian pathway. Before modeling, a series of preprocessing steps are employed to detect the moving objects. Multilayer Perceptron (MLP) is used to classify the parameters of GARCH model as normal event or abnormal event. Results show that the events are modeled by GARCH in an efficient manner which provides promising results for AED.

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Section: Introductionmentioning

confidence: 99%

Abnormal Event Detection in Pedestrian Pathway Using Garch Model and MLP Classifier

Pattnaik¹

2019

International Journal of Advances in Signal and Image Sciences

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High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis

Ardelean,

Weyrich

2024

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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Anomaly Analysis in Images and Videos: A Comprehensive Review

Tran

et al. 2022

ACM Comput. Surv.

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Anomaly analysis is an important component of any surveillance system. In recent years, it has drawn the attention of the computer vision and machine learning communities. In this article, our overarching goal is thus to provide a coherent and systematic review of state-of-the-art techniques and a comprehensive review of the research works in anomaly analysis. We would like to provide a broad vision of computational models, datasets, metrics, extensive experiments, and what anomaly analysis can do in images and videos. Intensively covering nearly 200 publications we review i) anomaly related surveys, ii) taxonomy for anomaly problems, iii) the computational models, iv) the benchmark datasets for studying abnormalities in images and videos, and v) the performance of state-of-the-art methods in this research problem. In addition, we provide insightful discussions and pave way to the future work.

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An Overview of Anomaly Detection in Video Surveillance

Cited by 4 publications

References 25 publications

Abnormal Event Detection in Pedestrian Pathway Using Garch Model and MLP Classifier

Abnormal Event Detection in Pedestrian Pathway Using Garch Model and MLP Classifier

High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis

Anomaly Analysis in Images and Videos: A Comprehensive Review

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