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THE DETECTION OF PERSONS IN CLUTTERED BEACH SCENES USING DIGITAL VIDEO IMAGERY AND NEURAL NETWORK-BASED CLASSIFICATION

STEVE GREEN

School of Information and Communication Technology, Griffith University, Gold Coast, Queensland 9726, Australia

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MICHAEL BLUMENSTEIN

School of Information and Communication Technology, Griffith University, Gold Coast, Queensland 9726, Australia

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MATTHEW BROWNE

CSIRO Mathematical and Information Sciences, Cleveland, Queensland 4163, Australia

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, and

RODGER TOMLINSON

Griffith Centre for Coastal Management, Griffith University, Gold Coast, Queensland 9726, Australia

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https://doi.org/10.1142/S1469026806001927Cited by:1 (Source: Crossref)

Abstract

This paper presents an investigation into the detection and quantification of persons in real-world beach scenes for the automated monitoring of public recreation areas. Aside from the obvious use of video and digital imagery for surveillance applications, this research focuses on the analysis of images for the purpose of predicting trends in the intensity of public usage at beach sites in Australia. The proposed system uses image enhancement and segmentation techniques to detect objects in cluttered scenes. Following these steps, a newly proposed feature extraction technique is used to represent salient information in the extracted objects for training of a neural network. The neural classifier is used to distinguish the extracted objects between "person" and "non-person" categories to facilitate analysis of tourist activity. Encouraging results are presented for person classification on a database of real-word beach scene images.

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