Memory access minimization for mean-shift tracking in mobile devices

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.