Recently, visual tracking has been formulated as a classification problem whose task is detecting the object form the scene with a binary classifier. And online boosting, which adapts the binary classifier to appearance changes by online feature selection, has been investigated by researchers. However, online boosting generally suffers from drifting if the tracking error accumulates. To reduce tracking error, separability-maximum boosting (SMBoost), together with a two stage online boosting paradigm (online SMBoost), is proposed and applied to visual tracking. SMBoost uses a separability based cost function that defined on the statistics. And online boosting is therefore split into two individual stages: online statistics estimating and separability-maximum classifier training. Experiment on UCI machine learning datasets shows that SMBoost is more accurate than batch AdaBoost and its online variation. And benchmark on public sequences indicates that feature selection with online SMBoost is more effective and robust comparing with previous online boosting algorithm. To track a visual object stably, online SMBoost saves more than 50% classifier complexity, and achieves 108 fps.