The conventional subspaces based tracking methods usually have low precision of object matching and tracking

because they lose the inherent partial structure and neighborhood information.In this paper

an incremental tensor subspace learning algorithm is proposed to model and update the object appearance in tensor subspace.Simultaneously

by combining the proposed learning algorithm with Bayesian inference

an adaptive object tracking method is presented.Firstly

we represented the appearance of the object in tensor subspace;secondly

obtained the optimal estimation of the state parameters by Bayesian inference;finally updated the tensor subspace by using the optimal observation.Due to the construction information is maintained

the proposed method is able to track targets effectively and robustly under pose variation

short-time occlusion and large lighting and so on in the experiments.