关键词: 目标跟踪, 在线加权多示例学习, Haar-like特征, 稀疏表示

Abstract: In order to improve the accuracy of target tracking in video sequences, a tracking algorithm combining low-dimensional Haar-like feature and Online Weighted Multiple Instance Learning（OWMIL） is proposed. The image in training set is clipped to construct positive and negative sample sets. The low-dimensional Haar-like feature is extracted by sparse coding to represent the target. A weak classifier set is generated by online learning the local sparse features of these positive and negative samples, and an example weighting method is used to promote the learning process. A strong classifier is generated for target tracking in test video. Experimental results show that the proposed algorithm achieves excellent results under the influence of rotation, illumination and scale change.

Key words: target tracking, online weighted multiple instance learning, Haar-like feature, sparse representation