Median flow aided online multi-instance learning visual tracking
中值流辅助在线多示例目标跟踪

To satisfy the stringent requirements of the object tracking performance in the robot's learning-from-demonstration-framework, a new tracking algorithm that can deal with fast motions, occlusions, and drifts, is proposed. First, the Median-Flow method is used to predict the position-shift of the object and the Gaussian weight of each patch. Then, the search-region is modified and the object is located by the online multi-instance learning classifier. Afterwards, the likelihood of each patch is calculated. Finally, the results are combined under the Bayes framework to get the best prediction by exhaustive search and the online classifier is updated. Experiments in several commonly used test videos show that our method outperforms the other state-of-the-art tracking methods, especially for fast motion and drifts. Furthermore, the proposed method can run in real-time.