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Implementation of an object-detection algorithm on a CPU+GPU target
KTH, School of Information and Communication Technology (ICT).
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Systems like autonomous vehicles may require real time embedded image processing under hardware constraints. This paper provides directions to design time and resource efficient Haar cascade detection algorithms. It also reviews some software architecture and hardware aspects. The considered algorithms were meant to be run on platforms equipped with a CPU and a GPU under power consumption limitations. The main aim of the project was to design and develop real time underwater object detection algorithms. However the concepts that are presented in this paper are generic and can be applied to other domains where object detection is required, face detection for instance. The results show how the solutions outperform OpenCV cascade detector in terms of execution time while having the same accuracy.

Abstract [sv]

System så som autonoma vehiklar kan kräva inbyggd bildbehandling i realtid under hårdvarubegränsningar. Denna uppsats tillhandahåller anvisningar för att designa tidsoch resurseffektiva Haar-kasad detekterande algoritmer. Dessutom granskas en del mjukvaruarkitektur och hårdvaruaspekter. De avsedda algoritmerna är menade att användas på plattformar försedda med en CPU och en GPU under begränsad energitillgång. Det huvudsakliga målet med projektet var att designa och utveckla realtidsalgoritmer för detektering av objekt under vatten. Dock är koncepten som presenteras i arbetet generiska och kan appliceras på andra domäner där objektdetektering kan behövas, till exempel vid detektering av ansikten. Resultaten visar hur lösningarna överträffar OpenCVs kaskaddetektor beträffande exekutionstid och med samtidig lika stor träffsäkerhet.

Place, publisher, year, edition, pages
2016. , 42 p.
Series
TRITA-ICT-EX, 2016:202
Keyword [en]
Object detection, Parallel processing, GPGPU, Haar-like features
Keyword [sv]
Objektdetektering, Parallel bearbetning, GPGPU, Haar-lika egenskaper
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-206178OAI: oai:DiVA.org:kth-206178DiVA: diva2:1091687
Subject / course
Information and Communication Technology
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2017-04-27 Created: 2017-04-27 Last updated: 2017-06-07Bibliographically approved

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