Online feature subset selection for object tracking
Jinwei Yuan, Farokh Bastani · 2014
Online tracking often encounters the drift problem due to factors such as occlusion, motion blur, pose and illumination changes. While much success has been demonstrated, it is still a challenging task to design a robust appearance model for the tracker to effectively solve the drift problem. In this paper, we propose a novel object tracking framework with appearance model based on an effective online feature subset selection scheme which combines a support vector machine recursive feature elimination (SVM-RFE) procedure and a multiple instance learning (MIL) optimization process. The SVM-RFE procedure can help find the most informative subset from a feature pool, while the MIL optimization process helps to solve the ambiguity problem. Experiments on the benchmark dataset and comparisons with the latest state-of-the-art trackers validate the advantage of our approach.