Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Hardware Accelerated Particle Filter for Lane Detection and Tracking in OpenCL
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A road lane detection and tracking algorithm is developed, especially tailored to run on high-performance heterogeneous hardware like GPUs and FPGAs in autonomous road vehicles. The algorithm was initially developed in C/C++ and was ported to OpenCL which supports computation on heterogeneous hardware.A novel road lane detection algorithm is proposed using random sampling of particles modeled as straight lines. Weights are assigned to these particles based on their location in the gradient image. To improve the computation efficiency of the lane detection algorithm, lane tracking is introduced in the form of a Particle Filter. Creation of the particles in lane detection step and prediction, measurement updates in lane tracking step are computed parellelly on GPU/FPGA using OpenCL code, while the rest of the code runs on a host CPU. The software was tested on two GPUs - NVIDIA GeForce GTX 660 Ti & NVIDIA GeForce GTX 285 and an FPGA - Altera Stratix-V, which gave a computational frame rate of up to 104 Hz, 79 Hz and 27 Hz respectively. The code was tested on video streams from five different datasets with different scenarios of varying lighting conditions on the road, strong shadows and the presence of light to moderate traffic and was found to be robust in all the situations for detecting a single lane.

Place, publisher, year, edition, pages
2014. , p. 99
Keyword [en]
Technology, GPGPU, FPGA, Image Processing, Computer Vision, Parallel Computing, Road Lane Detection, Lane Tracking, Particle Filter, OpenCL
Keyword [sv]
Teknik
Identifiers
URN: urn:nbn:se:ltu:diva-53411Local ID: a6d2bc41-2045-4d74-976d-a593310c8313OAI: oai:DiVA.org:ltu-53411DiVA: diva2:1026785
External cooperation
Subject / course
Student thesis, at least 30 credits
Educational program
Space Engineering, master's level
Supervisors
Examiners
Note
Validerat; 20140128 (global_studentproject_submitter)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

Open Access in DiVA

fulltext(13835 kB)119 downloads
File information
File name FULLTEXT02.pdfFile size 13835 kBChecksum SHA-512
9ff02b85ae78ea60e3a6adb515016e9f953b49cde3837d0a7046b7944268ae59b8775fccfcb896229a656be9c508113192c14d4c684c8a4b4151458c360ea899
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Madduri, Nikhil

Search outside of DiVA

GoogleGoogle Scholar
Total: 119 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 328 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf