Performance analysis and GPU parallelisation of ECO object tracking algorithm

Main Article Content

Ugur Taygan
https://orcid.org/0000-0002-2003-2187
Adnan Ozsoy
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, (12), 109-118. https://doi.org/10.18844/gjpaas.v0i12.4991
Section
Articles