关键词: 多核学习, 目标跟踪, 提升方法, 复杂环境

Abstract: As traditional tracking algorithms fail to track target stably due to the external environment and the target motion caused deformation, a robust multiple kernel learning based algorithm is proposed. By introducing the Boosting method into the multiple kernel learning framework, building a pool of weak classifiers trained with complementary feature set and complementary kernel function set needs less samples comparing to the traditional multiple-kernel learning algorithms. Thus a multiple-kernel strong classifier is constructed by combining several weak classifiers selected from the weak classifier pool, which can correctly differentiate the target and background from the candidate patches even when the target is under notable occlusion and background clutters. Results of test on different video sequences show that when the tracked object is in complex environment, the proposed algorithm has higher tracking accuracy compared with OAB algorithm which similarly uses the Boosting method and the LOT algorithm which has a high tracking accuracy.

Key words: multiple-kernel learning, object tracking, Boosting method, complex environment