Abstract: The subspace constructing strategy of classic subspace-based tracking schemes is to select appropriate subspaces with maximum energy, in this strategy the discriminability between the target and background is neglected, so when the target and background have similar appearance the tracking system's performance may be degenerated. To solve the problems of IR image's low SNR and low contrast, a novel subspace selecting method was proposed based on analyzing the discriminability between the target and background. The IR object tracking process was realized by the particle filter with the provided subspace selecting strategy. In this case, based on the prior knowledge of the particles distributions and the target state, different subspace's tracking ability by considering both the feature difference and the particles' approximation level to the target was estimated firstly, then the optimal subspaces were selected to realized the IR target tracking. Experiments on several complex scenes indicate that the proposed algorithm has better performance than the classic one.