Infrared small target tracking is susceptible to similar target and background. To address this problem

an algorithm for infrared small target tracking based on gray and velocity cue integration is proposed. The algorithm enhances the target with high pass filter

and calculates velocity of target by principal component analysis. Target tracking is achieved by a cascade particle filter which consists of two stages of importance sampling. At the first stage

the states of target are crudely estimated with gray kernel histogram in high pass filter space. The velocity cues are used to precisely calculate the states of target in the second stage. The experimental results show that the proposed algorithm has stronger ability to resist to interference of noise

and significantly improves the tracking accuracy in comparison with existing tracking algorithms.