Robust object tracking via online learning of adaptive appearance manifold

Abstract

Appearance modeling plays a critical role in robust object tracking, which should be adaptive to various appearance changes. We propose a new appearance model based on adaptive appearance manifold for object tracking. The adaptive appearance manifold consists of several submanifolds and each is approximated with a low dimensional linear subspace. The initial appearance model is constructed using location information of target object in the first frame, and no prior knowledge is needed. We design an efficient dynamic structure for the adaptive appearance manifold, which can reduce time of comparison between a new observation and the appearance model. The appearance model is incrementally learned online using the input sequence image. We integrate our new appearance model with the particle filtering framework. Several public challenging videos are used to test our tracking algorithm. The experimental results demonstrate that our algorithm is robust to illumination change, pose variation, partial occlusion and clutter background. And the speed of our algorithm is also very fast.