The challenges of robust 24/7 video surveillance systems

Boghossian, B.; Black, Justin

doi:10.1049/ic:20050066

Cited by 2 publications

(1 citation statement)

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“…Several works have studied the state-of-the-art deep models for video anomaly detection and have classified them based on the type of model and criteria of detection (Kiran et al [2018], Chong and Tay [2015]). The challenges of robust 24/7 video surveillance systems are discussed in detail by (Boghossian and Black [2005]). The lack of an explicit definition of an anomaly in real-life video surveillance is a significant issue that hampers the performance of DAD methods as well.…”

Section: Video Surveillancementioning

confidence: 99%

Deep Learning for Anomaly Detection: A Survey

Chalapathy,

Chawla

2019

Preprint

381

452

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Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art deep anomaly detection research techniques into different categories based on the underlying assumptions and approach adopted. Within each category, we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. Besides, for each category, we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting deep anomaly detection techniques for real-world problems.

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Section: Video Surveillancementioning

confidence: 99%