Moving object tracking with feature learning and inheriting
Moving object tracking with discriminative model is very popular in recent years, which focuses on online selecting highly informative features to maximize the separability between object and background. An adapted particle filter tracker with online learning and inheriting discriminative model is proposed in this paper. Top-ranked discriminative features are selected into appearance model by Online Weighted Multiple Instance Learning, and some stable features are inherited to subsequent frames. This discriminative appearance model with feature learning and feature inheriting helps particle filter to evaluate particle more effectively, and particle filter can make online learning to generate a better representative set of training examples. Experiments on challenging video sequences show that our method achieved competitive results compared with state-of-the-art online learning-based discriminative tracking approaches.