Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems

Chowdhury, Sayeed Shafayet; Islam, Kazi Mejbaul; Noor, Rouhan

doi:10.48550/arxiv.2006.03733

Cited by 1 publication

(1 citation statement)

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“…The UAM is also been used for facial expression recognition where it captures the attributes of all expressions [58] Further, for autonomous vehicles like cars or UAVs (Unmanned Aerial Vehicles) it is very essential to distinguish between normal and abnormal states. Chowdhury et al estimates the degree of abnormality using an unsupervised heterogeneous system from real-time images and IMU (Inertial Measurement Unit) sensor data in a UAV [59]. They also demonstrated a CNN architecture to estimate an angle between a normal image and query image, to provide a measure of anomaly.…”

Section: Unsupervised Learningmentioning

confidence: 99%

Anomaly Detection using Edge Computing in Video Surveillance System: Review

Patrikar¹,

Parate²

2021

Preprint

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The current concept of Smart Cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and give a decent quality of life to its residents. To fulfill this need video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we surveyed various methodologies developed to detect anomalies in intelligent video surveillance. Firstly, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies developed for ease of understanding. Considering the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is considered a time-critical application of computer vision, our emphasis is on anomaly detection using edge devices and approaches explicitly designed for them. Further, we discuss the challenges and opportunities involved in anomaly detection at the edge.

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Section: Unsupervised Learningmentioning

confidence: 99%