UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System
| Autor: | Ming-Hwa Sheu, Chi-Chia Sun, Yao-Fong Huang, Yu-Syuan Jhang, Shin-Chi Lai, S. M. Salahuddin Morsalin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computational complexity theory
TK7800-8360 Computer Networks and Communications Computer science patch color group feature (PCGF) 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology embedded system Discriminative model UAV object tracking unmanned aerial vehicle (UAV) 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering 021101 geological & geomatics engineering business.industry Frame (networking) Object (computer science) Mixture model Frame rate Gaussian mixture model (GMM) Hardware and Architecture Control and Systems Engineering Feature (computer vision) Embedded system Video tracking Signal Processing 020201 artificial intelligence & image processing Electronics business |
| Zdroj: | Electronics, Vol 10, Iss 1864, p 1864 (2021) Electronics Volume 10 Issue 15 |
| ISSN: | 2079-9292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|