关键词: 多示例学习, 在线学习, 目标跟踪, 随机森林

Abstract: Multiple instance learning is a new mode of learning, which is different from the traditional machine learning. It has been used in the field of image retrieval, text classification in recent years. This paper proposes an online multi-instance learning algorithm, and applies it to the object tracking. The algorithm by constructing an online learning multi-instance classifier as the detector, without making a large number of samples for off-line training, and only needs to manually select the object in the first frame. It can automatically generate the positive samples and negative samples, and in the subsequent sequence of frames, automatically update the classifier based on tracking target. If the tracker misses the target or the target disappears from the scene, it can re-detect target and update the tracker, so it can effectively support the tracker to track the object. Experiments show that the proposed approach can track the target well in the complex background, the light change, camera jitter and some other complex conditions, and it has a better robustness for the occlusion.

Key words: multiple instance learning, online learning, object tracking, random forest