Conventional tracking methods describe the target with a bounding box.As the bounding box is likely to contain some background regions and will degrade the tracking performance, a superpixel tracking method via online multiple instance learning is proposed.In training stage, input frame is segmented into superpixels, which are divided into several instance bags with clear labels according to their location.The tracking is thus converted into a multiple instance learning problem.Then, online multiple instance learning is implemented with the algorithm.The maximum of instance bags log-likelihood function is calculated to get K best weak classifiers, which are combined into a strong classifier.In detection stage, a confidence map is generated by the strong classifier in the subsequent frame.Finally, the state of the tracking target is estimated with the confidence map in particle filter framework.The proposed method runs at a rate of 15 frames per second on a laptop.Extensive experimental results on challenging sequences show that the proposed method performs well in terms of robustness and accuracy, especially for the target under complex background, moving at high-speed or is occluded.Compared with the original superpixel tracking, the typical values of precision and success rate of the proposed method are increased by 21% and 26%, reaching 91% and 90%, respectively.