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.

中文翻译：

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基于补丁颜色组特征的无人机目标跟踪应用在嵌入式系统中

无人机 (UAV) 的判别目标跟踪系统广泛应用于众多应用中。虽然在该领域进行了大量研究，但在无人机机载嵌入式系统上实施低计算成本算法仍然具有挑战性。为了解决这个问题，我们在这项工作中使用补丁颜色组特征（PCGF）框架为无人机方法提出了一种低计算复杂度的判别对象跟踪系统。跟踪对象被分成几个不重叠的局部图像块，然后将特征提取到由高斯混合模型（GMM）组成的 PCGF 中。对象位置是通过与前一帧和当前帧的相似 PCGF 比较来计算的。通过四个方向特征扫描和动态阈值比较去除对象的背景PCGF，提高了性能精度。在速度执行方面，该算法在没有 GPU 加速器的 x64 CPU 平台上实现了 32.5 帧每秒 (FPS)，在 Raspberry Pi 4 上实现了 17 FPS。因此，这项工作可以被认为是实现低无人机机载嵌入式系统上的计算复杂度 PCGF 算法以改善飞行时间。