Probability Hypothesis Density Correlation Filter for Infrared Small Target Tracking

Tracking infrared (IR) small targets in real-time video sequences can be challenging due to the weak target information and heavy background clutter. To improve the accuracy and stability in infrared small target tracking, this letter proposes a novel method named the probability hypothesis density correlation filter (PHDCF). A stable tracking condition is introduced as a switch between the spatial–temporal regularized correlation filter (STRCF) and the derivative probability hypothesis density (PHD) filter. When the target is submerged in clutter, the PHD filter generations candidate estimates of the target according to prior tracking information, and a joint weighting by fusing the response score and motion similarity is performed to decide the new state of the target. Experimental results on real IR sequences illustrate that the PHDCF exceeds STRCF by 6.7% and 12.5% in terms of the success and precision rates, respectively, and reaches better precision and robustness than the baseline methods.