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0-0. The Detection of Persons in Cluttered Beach Scenes Using Digital Video Imagery And Neural Network-Based

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  • March 27, 2007 14:53 WSPC/157-IJCIA 00192

    International Journal of Computational Intelligence and ApplicationsVol. 6, No. 2 (2006) 149160c Imperial College Press

    THE DETECTION OF PERSONS IN CLUTTERED BEACHSCENES USING DIGITAL VIDEO IMAGERY AND NEURAL

    NETWORK-BASED CLASSIFICATION

    STEVE GREEN and MICHAEL BLUMENSTEIN

    School of Information and Communication TechnologyGrith University, Gold CoastQueensland 9726, Australia

    MATTHEW BROWNE

    CSIRO Mathematical and Information SciencesCleveland, Queensland 4163, Australia

    RODGER TOMLINSON

    Grith Centre for Coastal Management, Grith UniversityGold Coast, Queensland 9726, Australia

    Received 31 January 2006Revised 8 June 2006

    This paper presents an investigation into the detection and quantification of personsin 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 inthe intensity of public usage at beach sites in Australia. The proposed system usesimage enhancement and segmentation techniques to detect objects in cluttered scenes.Following these steps, a newly proposed feature extraction technique is used to representsalient information in the extracted objects for training of a neural network. The neuralclassifier is used to distinguish the extracted objects between person and non-personcategories to facilitate analysis of tourist activity. Encouraging results are presented forperson classification on a database of real-word beach scene images.

    Keywords: People detection; image segmentation; modified direction feature; video imageanalysis; beach imagery.

    1. Introduction

    This paper describes a novel person detection system for analyzing beach sceneimagery. Quantifying people on beaches can provide valuable information for localauthorities to estimate the number of persons using a beach on a particular day.People use beaches for exercise, relaxation, and social activities. The monitoring oflocal beach behavior can provide valuable information regarding whether currentamenities at a particular site are sucient to meet changing levels of demand. Mostmajor beaches around Australia, and the world, have World Wide Web cameras

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    150 S. Green et al.

    (sometimes also called web cams) that provide local beach information accessiblethrough the Internet. Coastalwatcha Pty. Ltd. provides this service in Australia viaa national network of remote cameras.

    The remote video feeds that Coastalwatch provide oer up-to-date informationon surf and current weather conditions at many beach locations around Australia.The streamed images from Coastalwatch web cams are of a low resolution. There-fore, aside from the inherent diculties in dealing with variable outdoor imagery,processing low quality images provides quite a challenge for any automated humanquantication system. The low quality beach imagery is one of the main constraintsupon this research. People (and other objects) within these images can, in somecases, only be 10 or 15 pixels in height.

    Another important aspect of detecting people on beaches is that of safety.Beaches can provide a dangerous environment for people of all ages.1 Currently, life-guards provide assistance for swimmers who encounter trouble between the ags,which are designated as patrolled areas. This requires human surveillance of thebeach to notify a lifeguard when a swimmer is distressed or in trouble. If a sys-tem could be developed to monitor and detect erratic or uncharacteristic swimmerbehavior and notify the lifeguard, this would provide extra safety measures forswimmers.

    The remainder of this paper is organized into four main sections. Section 2 givesan overview of existing techniques in the literature relating to object and persondetection, Sec. 3 provides a detailed description of the proposed person detectionsystem, in Sec. 4, the results attained using the proposed system are presented andnally conclusions and future work are provided in Sec. 5.

    2. Overview of Existing Techniques for Person and ObjectDetection

    The automated detection of persons and their behavior in beach scenes is a novelapplication in the eld of video surveillance. However, a number of techniques andsystems have been proposed for automated analysis of humans in other indoor andoutdoor situations. Some of these are reviewed and detailed in the paragraphs thatfollow.

    Schoeld et al.2 proposed a system for analyzing video imagery to count personson the oors of buildings for improving the eciency of elevator systems. They haveused intelligent techniques for identifying the background of a scene.3 The limitationof this system was that it was based indoors. However, a number of systems havebeen proposed for dealing with outdoor conditions, which have proved to be farmore variable. Iketani et al.4 propose a system for real-time detection of intruders(persons) in dicult, outdoor and cluttered scenes using information from videoimagery over space and time. Sacchi et al.5 present advanced image processing tools

    ahttp://www.coastalwatch.com

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  • March 27, 2007 14:53 WSPC/157-IJCIA 00192

    Detection of Persons in Cluttered Beach Scenes 151

    for remote monitoring of a tourist site involving the counting of persons in outdoorscenes. Bartolini et al.6 propose a system for automatically counting persons gettingin and o a bus using image sequence analysis for allocating appropriate resourceson bus lines. Pai et al.7 present a system for pedestrian tracking using vision-basedtechniques to prevent trac accidents. Finally, person tracking in complex sceneshas emerged as a challenging problem in the area of video surveillance. A numberof systems have recently been proposed in the literature, which address this eld ofresearch.810

    Aside from the quantication of persons, some studies have also been performedfor the purpose of quantifying and tracking the behavior of motor vehicles. Thesestudies (mainly dealing with outdoor imagery) are of relevance to the present studyas the techniques that are detailed for detection, monitoring and classication aretransferable to tracking human objects in outdoor scenes.

    Tai et al.11 present an image tracking system, which locates motorcycles andother vehicles for trac monitoring and accident detection at road intersections.Another system recently proposed by Ha et al.12 has employed a neural network-based edge detector for vehicle detection and trac parameter estimation (vehiclecount, class and speed) in an image-based trac monitoring system. Other systemssuch as that of Wohler and Anlauf,13 have been used to assist drivers in automobilesthrough the detection of overtaking vehicles. They employed an adaptable time-delay articial neural network (ANN) to analyze complete image sequences. Finally,some researchers have utilized additional indicatory information for the purposeof vehicle detection. For example, Altmann et al.14 have developed a system todetect military vehicles using acoustic and seismic information for application indisarmament and peacekeeping.

    A number of current object detection systems, operating within static imagery,employ an exhaustive search technique to locate regions of interest.1517 Theexhaustive search technique is computationally expensive, and therefore eliminat-ing regions of non-interest is important to reduce not only the false positive rate,but also to reduce the computation time of the search. Our proposed system cur-rently reduces this search time by removing background information, and then onlysearches foreground regions that seem promising. Further details pertaining to theproposed system are discussed in the next section.

    3. System Overview

    This research describes a model for the automatic detection of people on beaches.The proposed system uses a classication-based strategy, which searches gray-scale images of beach scenes for potential objects of interest. The sub-imagesextracted are then processed to determine whether a person object has been found.Each sub-image is processed by using an edge detector, feature extractors andnally a neural-based classier. An overview of the entire system is presented inFig. 1.

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  • March 27, 2007 14:53 WSPC/157-IJCIA 00192

    152 S. Green et al.

    Fig. 1. System overview.

    3.1. Object detection and segmentation

    The rst important stage for segmenting objects out of a complex scene is to detectthe object boundaries in t