Robust Multi-scale Prior L-K Tracking Based on Local Features

Abstract

Due to the limitation of algorithm principle, inverse compositional L-K algorithm always fails to track fast moving targets. An improved tracking method based on multi-scale motion prior information is proposed in this paper. The multi-scale priori error Jacobi matrix is calculated offline by artificially constructed multi-scale disturbance. The target searching strategy based on multi-scale stratification can greatly increase the convergence range of the algorithm. At the same time, in order to improve the stability of global feature tracking in the case of local occlusion, the pyramid iterative L-K tracking method based on local feature is employed. Experimental results demonstrate outstanding stability of our method for fast moving targets. And by taking advantage of local gray information around feature points, our algorithm can still effectively track the target in the presence of partial occlusion.