UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System

Abstract

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.

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

“…The data recorded with Raspberry Pi 4, clearly indicates the accuracy in UAV object detection [199]. For an object tracking-based UAV mission, when the Patch Color Group Feature (PCGF) framework was embedded on a Raspberry Pi 4, it resulted in 17 FPS offering a good execution speed with low PCGF computational complexities [200]. Older Raspberry Pi models, such as Raspberry Pi 2 B+, are effective in illustrating the relationship between time constraints of real-time systems and the analysis of temporary computational complexity [201], hence better managing failure possibilities in real-time processes.…”