Abstract: A novel visual tracking method based on artificial fish swarm algorithm on Riemannian manifold metric was proposed. The new algorithm can well deal with the interactive occlusion, and consume less computation load comparing with global exhaustive search, both of which arc the limits of classical covariance descriptor tracker. hhe paper used co- variance descriptor combining with object information of position, color, and gradient to enhance the adaptability to change of gesture and illumination changing. hhc artificial fish swarm algorithm was utilized to find the best matching between object and candidate. Its parallel operation and global search ability improves the effectiveness of processing and can be more robust to occlusion. The experimental results show that the proposed method is more robust for visual tracking under complex scene.

Key words: Visual tracking, Covariance descriptor, Artificial fish swarm algorithm, Mahalanobis distance, Riemannian manifold