关键词: 多示例学习, 1-norm SVM, 分类器, 目标跟踪

Abstract: For the poor robustness and target drift problem of the most existing tracking algorithms in complex environment, an improved target tracking algorithm based on multiple instance learning is proposed. The MIL tracker ignores the differences of each sample in the process of computing the bag probability, which declines the performance of classifier, and there exists complex problem in choosing the weak classifier. This paper solves these problems by computing the importance of each sample to bag probability based on the 1-norm SVM method. Then, it adopts inner product method to compute the log-likelihood of bag in the process of choose weak classifier, which is benefit to reduce the computing complexity. Experimental results show that the proposed algorithm performs well with strong robustness and high tracking accuracy under the complicated environments such as occlusion, rotation, pose and illumination change.

Key words: multiple instance learning, 1-norm SVM, classifier, object tracking