Higher order partial least squares for object tracking: A 4D-tracking method

Abstract

Object tracking has a wide range of applications and great efforts have been spent to build the object appearance model using image features encoded in a vector as observations. Since a video or image sequence is intrinsically a multi-dimensional matrix or a high-order tensor, these methods cannot fully utilize the spatial-temporal correlations within the 2D image ensembles and inevitably lose a lot of useful information. In this paper, we propose a novel 4D object tracking method via the higher order partial least squares (HOPLS) which is a generalized multi-linear regression method. To do so, we first represent each training and testing example as a set of image instances of a target or background object. Then, we view object tracking as a multi-class classification problem and construct the 4D data matrix and 2D labeling matrix for HOPLS. Furthermore, we use HOPLS to adaptively learn low-dimensional discriminative feature subspace for object representation. Finally, a simple yet effective updating schema is used to update the object appearance model. Experimental results on challenging video sequences demonstrate the robustness and effectiveness of the proposed 4D tracking method.