CLUMOC: Multiple Motion Estimation by Cluster Motion Consensus

Yinan Yu, Weiqiang Ren, Yongzhen Huang, Kaiqi Huang, Tieniu Tan

Abstract: In this paper, we present techniques for robust multiple motions estimation based on dual consensus via clustering in both the image spatial space and the motion parameter space. Starting from traditional Random Samples Consensus algorithm, we novelly propose the CLUster MOtion Consensus (CLUMOC) to extract robust motions. The proposed algorithm has two advantages: 1), instead of random samples, the CLUMOC employs clustering in initial sample selection, which can remove outliers from correct pairs of motion, 2), CLUMOC automatically decides the number of motions, by employing competition among motion and samples, that each motion needs to compete for matching pairs and each pair of matching competes for motions. The experimental results show that the proposed method is effective and efficient under various situations.