Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/136744
Title: UNSUPERVISED DETECTION AND LOCALIZATION OF ANOMALOUS MOTION PATTERNS IN SURVEILLANCE VIDEO
Authors: ABDULLAH AHMAD TALEB ABUOLAIM
Keywords: computer vision, anomaly detection, unsupervised method, video surveillance, cctv camera, clustering
Issue Date: 14-Jul-2017
Source: ABDULLAH AHMAD TALEB ABUOLAIM (2017-07-14). UNSUPERVISED DETECTION AND LOCALIZATION OF ANOMALOUS MOTION PATTERNS IN SURVEILLANCE VIDEO. ScholarBank@NUS Repository.
Abstract: An important application in surveillance is to apply computerized methods to automatically detect anomalous activities and then notify the security officers. Many methods have been proposed for anomaly detection with varying degree of accuracy. They can be characterized according to the approach adopted, which is supervised or unsupervised, and the features used. Unfortunately, existing literature has not elucidated the essential ingredients that make the methods work as they do, despite the fact that tests have been conducted to compare the performance of various methods. This thesis attempts to fill this knowledge gap by studying the videos tested by existing methods and identifying key components required by an effective unsupervised anomaly detection algorithm. Our comprehensive test results show that an unsupervised algorithm that captures the key components can be relatively simple and yet perform equally well or better compared to existing methods.
URI: http://scholarbank.nus.edu.sg/handle/10635/136744
Appears in Collections:Master's Theses (Open)

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