基于多示例学习和随机蕨丛检测的在线目标跟踪

基于检测的目标跟踪方法目前在计算机视觉领域受到了广泛的关注，这类方法通过训练判别分类器将目标对象从背景中分离出来；分类器的训练是根据当前的跟踪状态从当前帧中提取正负样本来进行，但训练样本的不准确将导致分类器退化产生漂移。该文提出一种能够有效克服目标漂移的跟踪算法，采用检测器和跟踪器相结合的框架，利用中值流算法作为跟踪器，提高跟踪点的可靠性；级联若干个随机蕨弱分类器构成强分类器作为检测器；用在线多示例学习方法更新检测器，提高检测精度；最后将检测器、跟踪器的结果相融合得到最终的目标位置。实验结果表明，与其它方法相比，该方法对目标漂移有更强的鲁棒性。

Online Target Tracking Based on Mulitiple Instance Learning and Random Ferns Detection

Recently, a class of tracking techniques called tracking by detection receive much attention in computer vision. These methods train a discriminative classsifier to separate the object from the background. The classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker lead to incorrectly labeled training examples, which degrade the classifier and cause drift. In this paper, an effective algorithm is proposed to overcome the target drift. It takes the framework of tracking by detection. Median Flow (MF) is used as a tracker to improve the reliability of the tracking point; the detector is constituted with several weak classifiers of random ferns to cascade, and it is updated with online Multiple Instance Learning (MIL). Finally the detector and tracking results are integrated to get the target location. Experiments on a number of challenging video clips show that the proposed method outperforms some state-of-the-art tracking methods, especially for fast motion and drifts.