Performance analysis and GPU parallelisation of ECO object tracking algorithm

Article Sidebar

Published: Apr 30, 2020
Updated: 2020-04-30
Versions:
2020-04-30 (1)
DOI: https://doi.org/10.18844/gjpaas.v0i12.4991

Main Article Content

Ugur Taygan
Hacettepe University, Turkey
https://orcid.org/0000-0002-2003-2187
Adnan Ozsoy
Hacettepe University, Turkey
https://orcid.org/0000-0002-0302-3721

Abstract

The classification and tracking of objects has gained popularity in recent years due to the variety and importance of their application areas. Although object classification does not necessarily have to be real time, object tracking is often intended to be carried out in real time. While the object tracking algorithm mainly focuses on robustness and accuracy, the speed of the algorithm may degrade significantly. Due to their parallelisable nature, the use of GPUs and other parallel programming tools are increasing in the object tracking applications. In this paper, we run experiments on the Efficient Convolution Operators object tracking algorithm, in order to detect its time-consuming parts, which are the bottlenecks of the algorithm, and investigate the possibility of GPU parallelisation of the bottlenecks to improve the speed of the algorithm. Finally, the candidate methods are implemented and parallelised using the Compute Unified Device Architecture.


 


Keywords: Object tracking, parallel programming.

Downloads

Download data is not yet available.

Article Details

How to Cite
Taygan, U., & Ozsoy, A. (2020). Performance analysis and GPU parallelisation of ECO object tracking algorithm. New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, 2020(12), 109–118. https://doi.org/10.18844/gjpaas.v0i12.4991
Issue
No. 12 (2020): Selected Paper of 10th World Conference on Innovation and Computer Science (INSODE-2020)
Section
Articles
Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).