Due to the development of artificial intelligence and computer vision technology, many autonomous drones have been studied. However, computer vision technology requires high performance CPU due to its high complexity, and battery consumption is so high that drones are constrained to fly for a long time. Therefore, low-power mobile devices require tracking algorithms that minimize battery consumption. In this paper, we propose a mean-shift based tracking algorithm that minimizes memory access to reduce battery consumption. To accomplish this, we minimize the number of memory accesses by using an algorithm that divides the direction of the mean-shift vector into eight, and calculates the sum of the density maps only for the new area without calculating the sum of the density maps for the already calculated area. It is possible to increase the calculation efficiency by lowering the memory access cost. Experimental results show that the proposed method is more efficient than the existing method.

由于人工智能和计算机视觉技术的发展，已经研究了许多无人驾驶无人机。但是，计算机视觉技术由于具有很高的复杂性而需要高性能的CPU，并且电池的消耗是如此之高，以至于无人机不得不长时间飞行。因此，低功耗移动设备需要跟踪算法以最大程度地减少电池消耗。在本文中，我们提出了一种基于均值漂移的跟踪算法，该算法可最大程度地减少内存访问以减少电池消耗。为此，我们使用一种算法将内存移动次数减至最少，该算法将均值偏移矢量的方向划分为八个，并仅针对新区域计算密度图的总和，而无需为新区域计算密度图的总和。已经计算的面积。通过降低存储器访问成本可以提高计算效率。实验结果表明，该方法比现有方法具有更高的效率。