Viability of Feature Detection on Sony Xperia Z3 using OpenCL
Context. Embedded platforms GPUs are reaching a level of perfor-mance comparable to desktop hardware. Therefore it becomes inter-esting to apply Computer Vision techniques to modern smartphones.The platform holds different challenges, as energy use and heat gen-eration can be an issue depending on l...
Main Authors: | , |
---|---|
Format: | Others |
Language: | English |
Published: |
Blekinge Tekniska Högskola, Institutionen för kreativa teknologier
2015
|
Subjects: | |
Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10388 |
id |
ndltd-UPSALLA1-oai-DiVA.org-bth-10388 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-UPSALLA1-oai-DiVA.org-bth-103882018-01-12T05:10:43ZViability of Feature Detection on Sony Xperia Z3 using OpenCLengDanielsson, MaxSievert, ThomasBlekinge Tekniska Högskola, Institutionen för kreativa teknologierBlekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknikBlekinge Tekniska Högskola, Institutionen för kreativa teknologierBlekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik2015GPUFeature DetectionFeature DescriptionEmbedded DeviceComputer SciencesDatavetenskap (datalogi)Context. Embedded platforms GPUs are reaching a level of perfor-mance comparable to desktop hardware. Therefore it becomes inter-esting to apply Computer Vision techniques to modern smartphones.The platform holds different challenges, as energy use and heat gen-eration can be an issue depending on load distribution on the device. Objectives. We evaluate the viability of a feature detector and de-scriptor on the Xperia Z3. Specifically we evaluate the the pair basedon real-time execution, heat generation and performance. Methods. We implement the feature detection and feature descrip-tor pair Harris-Hessian/FREAK for GPU execution using OpenCL,focusing on embedded platforms. We then study the heat generationof the application, its execution time and compare our method to twoother methods, FAST/BRISK and ORB, to evaluate the vision per-formance. Results. Execution time data for the Xperia Z3 and desktop GeForceGTX660 is presented. Run time temperature values for a run ofnearly an hour are presented with correlating CPU and GPU ac-tivity. Images containing comparison data for BRISK, ORB andHarris-Hessian/FREAK is shown with performance data and discus-sion around notable aspects. Conclusion. Execution times on Xperia Z3 is deemed insufficientfor real-time applications while desktop execution shows that there isfuture potential. Heat generation is not a problem for the implemen-tation. Implementation improvements are discussed to great lengthfor future work. Performance comparisons of Harris-Hessian/FREAKsuggest that the solution is very vulnerable to rotation, but superiorin scale variant images. Generally appears suitable for near duplicatecomparisons, delivering much greater number of keypoints. Finally,insight to OpenCL application development on Android is given Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:bth-10388application/pdfinfo:eu-repo/semantics/openAccess |
collection |
NDLTD |
language |
English |
format |
Others
|
sources |
NDLTD |
topic |
GPU Feature Detection Feature Description Embedded Device Computer Sciences Datavetenskap (datalogi) |
spellingShingle |
GPU Feature Detection Feature Description Embedded Device Computer Sciences Datavetenskap (datalogi) Danielsson, Max Sievert, Thomas Viability of Feature Detection on Sony Xperia Z3 using OpenCL |
description |
Context. Embedded platforms GPUs are reaching a level of perfor-mance comparable to desktop hardware. Therefore it becomes inter-esting to apply Computer Vision techniques to modern smartphones.The platform holds different challenges, as energy use and heat gen-eration can be an issue depending on load distribution on the device. Objectives. We evaluate the viability of a feature detector and de-scriptor on the Xperia Z3. Specifically we evaluate the the pair basedon real-time execution, heat generation and performance. Methods. We implement the feature detection and feature descrip-tor pair Harris-Hessian/FREAK for GPU execution using OpenCL,focusing on embedded platforms. We then study the heat generationof the application, its execution time and compare our method to twoother methods, FAST/BRISK and ORB, to evaluate the vision per-formance. Results. Execution time data for the Xperia Z3 and desktop GeForceGTX660 is presented. Run time temperature values for a run ofnearly an hour are presented with correlating CPU and GPU ac-tivity. Images containing comparison data for BRISK, ORB andHarris-Hessian/FREAK is shown with performance data and discus-sion around notable aspects. Conclusion. Execution times on Xperia Z3 is deemed insufficientfor real-time applications while desktop execution shows that there isfuture potential. Heat generation is not a problem for the implemen-tation. Implementation improvements are discussed to great lengthfor future work. Performance comparisons of Harris-Hessian/FREAKsuggest that the solution is very vulnerable to rotation, but superiorin scale variant images. Generally appears suitable for near duplicatecomparisons, delivering much greater number of keypoints. Finally,insight to OpenCL application development on Android is given |
author |
Danielsson, Max Sievert, Thomas |
author_facet |
Danielsson, Max Sievert, Thomas |
author_sort |
Danielsson, Max |
title |
Viability of Feature Detection on Sony Xperia Z3 using OpenCL |
title_short |
Viability of Feature Detection on Sony Xperia Z3 using OpenCL |
title_full |
Viability of Feature Detection on Sony Xperia Z3 using OpenCL |
title_fullStr |
Viability of Feature Detection on Sony Xperia Z3 using OpenCL |
title_full_unstemmed |
Viability of Feature Detection on Sony Xperia Z3 using OpenCL |
title_sort |
viability of feature detection on sony xperia z3 using opencl |
publisher |
Blekinge Tekniska Högskola, Institutionen för kreativa teknologier |
publishDate |
2015 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10388 |
work_keys_str_mv |
AT danielssonmax viabilityoffeaturedetectiononsonyxperiaz3usingopencl AT sievertthomas viabilityoffeaturedetectiononsonyxperiaz3usingopencl |
_version_ |
1718605439902416896 |