One class support vector machine(OCSVM) was applied to classify the pixels of the infrared detectors, and it can detect the blind pixels by the random scenes. The blind pixel detection algorithms were reviewed in the beginning, and the imbalance distribution of the normal pixels and blind pixel was discussed in the following. The infrared image sequence was used to set up the OCSVM models and calculate the super sphere parameters, when the support vectors were represented by the Lagrangian coefficients. The OCSVM was an unsupervised method to cluster the pixels by the changing gray level and the random scenes. The super sphere model built by OCSVM would be refreshed by the updating image sequence, while the Lagrangian coefficients of the support vectors were recorded, so the blind pixels could be eventually classified by the statistic results of the preceding coefficients series. The mid-wave infrared 320×256 image sequence was taken as an example to illustrate the proposed method, and it got the same results as the black body calibration. It could conclude that the OCSVM used for the online modeling of the blind pixel detection of the infrared detectors is adaptive and self-refreshing, and it could improve the efficiency of the infrared system test.中国科学院上海技术物理研究所创新基金(2014-CX25)