Abnormal Object Detection Using Feedforward Model and Sequential Filters

Abnormal object detection and discrimisnation is a critical research area for vision-based surveillance systems. This paper proposes a novel algorithm for the detection and discrimination of abnormal objects, such as abandoned and stolen objects. The proposed algorithm consists of three stages and t...

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Hauptverfasser:	Jiman Kim, Bongnam Kang, Hai Wang, Daijin Kim
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Accuracy Conferences feedforward model Feedforward neural networks foreground region Image edge detection Nickel Object detection sequential filter static region Surveillance
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creator	Jiman Kim Bongnam Kang Hai Wang Daijin Kim
description	Abnormal object detection and discrimisnation is a critical research area for vision-based surveillance systems. This paper proposes a novel algorithm for the detection and discrimination of abnormal objects, such as abandoned and stolen objects. The proposed algorithm consists of three stages and three different filters. The three stages cooperate with each other using the feedforward model to enhance detection and discrimination performance, while the sequential filters efficiently reject falsely detected regions using three categories of information. The results of experiments conducted using public datasets indicate that the proposed algorithm is more accurate and has a lower false alarm ratio than the existing system.
doi_str_mv	10.1109/AVSS.2012.5
format	Conference Proceeding
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identifier	ISBN: 146732499X
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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Accuracy Conferences feedforward model Feedforward neural networks foreground region Image edge detection Nickel Object detection sequential filter static region Surveillance
title	Abnormal Object Detection Using Feedforward Model and Sequential Filters
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