关键词: 多示例学习（MIL）, 目标性测量, 弱分类器选择, 包概率计算, 目标跟踪

Abstract: For the problems that the multiple instance learning (MIL) tracking algorithm does not distinguish the differences of each sample when computing the bag probability and selects the weak classifiers by maximizing the log likelihood function, which reduce the performance of classifier and increase the complexity of the algorithm, this paper proposes a tracking algorithm based on objectness weighted multiple instance learning. First, the importance of each sample is measured by the objectness, which is also used to assign the weight for each instance. Then the weighted value is utilized for computing the final bag probability. In the phase of weak classifier selection, a maximized inner product between weak classifier and log likelihood function is adopted to select weak classifiers from weak classifier pool, and then these weak classifiers are combined into a strong classifier. All these strategies are beneficial for improving the tracking accuracy and reducing the computational complexity. By tracking the video sequences under different complex scenes, experimental results show that the proposed algorithm has strong robustness and high tracking accuracy compared with competing method.

Key words: multiple instance learning (MIL), objectness measure, weak classifier selection, bag probability calculation, object tracking