A weighted Multiple Instance Learning(MIL) tracking method was proposed to improve the precision and real-time quality of online MIL tracking algorithm. First, target samples and background samples around a selected target were collected. Weak classifiers were generated by online learning the features of collected samples. In order to get K best weak classifiers, the maximum of samples′ log-likelihood was calculated. Every weak classifier was weighted differently and K weak classifiers were combined into a strong classifier. Finally, new unclassified samples were picked from the newly formed frame. The obtained strong classifier was used to separate the target and background. The classifying results were mapped into probabilities and the location of the sample with the largest probability was the target location wanted. Experiments on variant videos show that the accurate rate of the proposed algorithm is 93% and the average frame rate is 25 frame/s when the object size is 43 pixel×36 pixel. Compared with the original MILtracking algorithm, the real-time quality of proposed method increases by 67%.