This paper presents a novel approach to particle filtering which minimizes the total tracking distortion by considering dynamic variance of proposal density and adaptive number of particles for each frame. Traditionally, particle filters use fixed variance of proposal density and fixed number of particles per frame. We first propose the tracking distortion measurement and then obtain the optimal particle number and memory size allocation equations under two different constraints. After that, the optimal particle number allocation equation is demonstrated in one-dimensional and two-dimensional object tracking. Experimental results show the improved performance of our power-aware particle filters in comparison to traditional particle filters. At last, we give the complete algorithm for real application and show the better performance. To the best of our knowledge, this paper is the first to consider the variant numbers of particles for each frame.