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Author:Tikkanen, Tommi
Title:People detection and tracking using a network of low-lost depth cameras
Ihmisvirtojen seuranta syvyyskameroilla
Publication type:Master's thesis
Publication year:2014
Pages:v + 49 + liitt. (+3)      Language:   eng
Department/School:Sähkötekniikan korkeakoulu
Main subject:Automaatio- ja systeemitekniikka   (AS3001)
Supervisor:Visala, Arto
Instructor:Korkalo, Otto
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201403061532
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  7462   | Archive
Keywords:People Detection
People-flow counting
Kinect
Arbitrary oblique view cameras
Smart cameras
Ihmisvirtojen seuranta
ihmisen havaitseminen kuvasta
älykamerat
Abstract (eng):Automatic people detection is a widely adopted technology that has applications in retail stores, crowd management and surveillance.
The goal of this work is to create a general purpose people detection framework.
First, studies on people detection, tracking and re-identification are reviewed.
The emphasis is on people detection from depth images.
Furthermore, an approach based on a network of smart depth cameras is presented.
The performance is evaluated with four image sequences, totalling over 20 000 depth images.
Experimental results show that simple and lightweight algorithms are very useful in practical applications.
Abstract (fin):Automaattinen ihmisten havainnointi on jo laajalti käytetty teknologia, jolla on sovelluksia esimerkiksi kaupan ja turvallisuuden aloilla.
Tämän diplomityön tarkoituksena on suunnitella yleiskäyttöinen järjestelmä ihmisten havainnointiin sisätiloissa.
Tässä työssä ensin esitetään kirjallisuudesta löytyvät ratkaisut ihmisten havainnointiin, seurantaan ja tunnistamiseen.
Painopiste on syvyyskuvaa hyödyntävissä havaitsemismenetelmissä.
Lisäksi esittellään kehitetty älykkäiden syvyyskameroiden verkko.
Havainnointitarkkuutta kokeillaan neljällä kuvasarjalla, jotka sisältävät yli 20 000 syvyyskuvaa.
Tulokset ovat lupaavia ja näyttävät, että yksinkertaiset ja laskennallisesti kevyet ratkaisut sopivat hyvin käytännön sovelluksiin.
ED:2014-03-06
INSSI record number: 48746
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