Traditional multiple instance learning (MIL) tracking utilizes self-learning procedures in tracking systems. Once an object gets lost in tracking

the interior classifier easily degenerates. To alleviate this problem

we propose an improved multiple instance learning tracking based on online feature selection (MILOFS). First

a very sparse random matrix is constructed to facilitate the feature initial process. With this matrix

the intrinsic attributes of the features projected from a high-dimension image can be preserved. Then

the loss function of a bag model is built with the Fisher linear discriminant model. The discriminative model of the bag is formed directly at the instance level with the response of each instance. Finally

the gradient descend rule is incorporated into the online boosting framework

and gradient boosting is employed to construct the selection strategy for strong classifiers. Comparison experiments under different scenarios reveal that the center location errors of Online AdaBoost(OAB)

Online Multiple Instance Learning Tracking (MIL-Track)

Weighted Multiple Instance Learning(WMIL) and Multiple Instance Learning with Online Feature Selection(MILOFS) are 36

23

24

and 13 pixels

respectively. Hence

the proposed method is robust and accurate regardless of changes in the illumination

occlusion condition

and target appearance in the outer environment. An improved MILOFS is proposed in this work. The proposed method integrated with a gradient boosting framework and online feature selection strategy effectively addresses the issue of classifier degeneration in traditional MIL tracking.