UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System
The discriminative object tracking system for unmanned aerial vehicles (UAVs) is widely used in numerous applications. While an ample amount of research has been carried out in this domain, implementing a low computational cost algorithm on a UAV onboard embedded system is still challenging. To addr...
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MDPI AG
2021-08-01
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Series: | Electronics |
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Online Access: | https://www.mdpi.com/2079-9292/10/15/1864 |
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author | Ming-Hwa Sheu Yu-Syuan Jhang S M Salahuddin Morsalin Yao-Fong Huang Chi-Chia Sun Shin-Chi Lai |
author_facet | Ming-Hwa Sheu Yu-Syuan Jhang S M Salahuddin Morsalin Yao-Fong Huang Chi-Chia Sun Shin-Chi Lai |
author_sort | Ming-Hwa Sheu |
collection | DOAJ |
description | The discriminative object tracking system for unmanned aerial vehicles (UAVs) is widely used in numerous applications. While an ample amount of research has been carried out in this domain, implementing a low computational cost algorithm on a UAV onboard embedded system is still challenging. To address this issue, we propose a low computational complexity discriminative object tracking system for UAVs approach using the patch color group feature (PCGF) framework in this work. The tracking object is separated into several non-overlapping local image patches then the features are extracted into the PCGFs, which consist of the Gaussian mixture model (GMM). The object location is calculated by the similar PCGFs comparison from the previous frame and current frame. The background PCGFs of the object are removed by four directions feature scanning and dynamic threshold comparison, which improve the performance accuracy. In the terms of speed execution, the proposed algorithm accomplished 32.5 frames per second (FPS) on the x64 CPU platform without a GPU accelerator and 17 FPS in Raspberry Pi 4. Therefore, this work could be considered as a good solution for achieving a low computational complexity PCGF algorithm on a UAV onboard embedded system to improve flight times. |
first_indexed | 2024-03-10T09:17:07Z |
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id | doaj.art-88e99f8f211a4cdaaf0363aeff1b368a |
institution | Directory Open Access Journal |
issn | 2079-9292 |
language | English |
last_indexed | 2024-03-10T09:17:07Z |
publishDate | 2021-08-01 |
publisher | MDPI AG |
record_format | Article |
series | Electronics |
spelling | doaj.art-88e99f8f211a4cdaaf0363aeff1b368a2023-11-22T05:32:08ZengMDPI AGElectronics2079-92922021-08-011015186410.3390/electronics10151864UAV Object Tracking Application Based on Patch Color Group Feature on Embedded SystemMing-Hwa Sheu0Yu-Syuan Jhang1S M Salahuddin Morsalin2Yao-Fong Huang3Chi-Chia Sun4Shin-Chi Lai5Department of Electronic Engineering, National Yunlin University of Science & Technology, Douliu 64002, TaiwanDepartment of Electronic Engineering, National Yunlin University of Science & Technology, Douliu 64002, TaiwanDepartment of Electronic Engineering, National Yunlin University of Science & Technology, Douliu 64002, TaiwanDepartment of Electronic Engineering, National Yunlin University of Science & Technology, Douliu 64002, TaiwanSmart Machinery and Intelligent Manufacturing Research Center, Department of Electrical Engineering, National Formosa University, Huwei 632301, TaiwanDepartment of Computer Science and Information Engineering, Nanhua University, Chiayi 62249, TaiwanThe discriminative object tracking system for unmanned aerial vehicles (UAVs) is widely used in numerous applications. While an ample amount of research has been carried out in this domain, implementing a low computational cost algorithm on a UAV onboard embedded system is still challenging. To address this issue, we propose a low computational complexity discriminative object tracking system for UAVs approach using the patch color group feature (PCGF) framework in this work. The tracking object is separated into several non-overlapping local image patches then the features are extracted into the PCGFs, which consist of the Gaussian mixture model (GMM). The object location is calculated by the similar PCGFs comparison from the previous frame and current frame. The background PCGFs of the object are removed by four directions feature scanning and dynamic threshold comparison, which improve the performance accuracy. In the terms of speed execution, the proposed algorithm accomplished 32.5 frames per second (FPS) on the x64 CPU platform without a GPU accelerator and 17 FPS in Raspberry Pi 4. Therefore, this work could be considered as a good solution for achieving a low computational complexity PCGF algorithm on a UAV onboard embedded system to improve flight times.https://www.mdpi.com/2079-9292/10/15/1864unmanned aerial vehicle (UAV)UAV object trackingGaussian mixture model (GMM)patch color group feature (PCGF)embedded system |
spellingShingle | Ming-Hwa Sheu Yu-Syuan Jhang S M Salahuddin Morsalin Yao-Fong Huang Chi-Chia Sun Shin-Chi Lai UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System Electronics unmanned aerial vehicle (UAV) UAV object tracking Gaussian mixture model (GMM) patch color group feature (PCGF) embedded system |
title | UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System |
title_full | UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System |
title_fullStr | UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System |
title_full_unstemmed | UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System |
title_short | UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System |
title_sort | uav object tracking application based on patch color group feature on embedded system |
topic | unmanned aerial vehicle (UAV) UAV object tracking Gaussian mixture model (GMM) patch color group feature (PCGF) embedded system |
url | https://www.mdpi.com/2079-9292/10/15/1864 |
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