An effective algorithm for motion estimation of human faces
In this paper, we propose a fast and accurate block-matching algorithm for motion estimation of human faces via Artificial Bee Colony (ABC) optimization. The mean square error (MSE) is often used as the matching metric in block matching, which, however, has the high computational cost in practice. By using ABC optimization, we introduce a novel and effective block-matching metric. We develop a block-matching algorithm based on the proposed matching metric to improve the motion estimation accuracy of human faces with lower computational cost. Experimental results show that our method could achieve significant improvements over state-of-the-art fast block matching methods for motion estimation, in terms of both estimation accuracy and computational complexity.