Robust object tracking via online multiple instance metric learning

This paper presents a novel object tracking method using online multiple instance metric learning to adaptively capture appearance variations. More specifically, we seek for an appropriate metric via online metric learning to match the different appearances of an object and simultaneously separate the object from the background. The drift problem caused by potentially misaligned training examples is alleviated by performing online metric learning under the multiple instance setting. Both qualitative and quantitative evaluations on various challenging sequences are discussed.