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We present a particle filter-based target tracking algorithm for FLIR imagery. A dual foreground and background model is proposed for target representation which supports robust and accurate target tracking and size estimation. A novel online feature selection technique is introduced that is able to adoptively select the optimal feature to maximize the tracking confidence. Moreover, a coupled particle filtering approach is developed for joint target tracking and feature selection in an unified Bayesian estimation framework. The experimental results show that the proposed algorithm can accurately track poorly-visible targets in FLIR imagery even with strong ego-motion. The tracking performance is improved when compared to the tracker with a foreground-based target model and without online feature selection.