Infrared target tracking in multiple feature pseudo-color image with kernel density estimation

Tracking targets in infrared images is a challenging subject due to the low contrast and severe noise. Kernel density estimation (KDE) with robust performance is one of the well-known tracking algorithms. In essence, tracking targets with KDE algorithm is tracking the statistical features of their pixels by the histograms. The universal KDE which can track any features of targets has not been developed. We propose a strategy which does not need to improve on the KDE algorithm itself, but it can make KDE track other features. We first map the features into the pixel intensity and create the feature images. Then these feature images are used to construct the multiple feature pseudo-color images (MFPCIs). The kernel density estimation algorithm tracks targets in MFPCIs can indirectly track these features. Experiments validate that the performance of tracking targets in MFPCIs outperforms that of tracking them in the original infrared images.

► We propose a strategy which can make the kernel density estimation (KDE) track other features besides the intensity feature.
► We construct the multiple feature pseudo-color images (MFPCIs) and track targets in MFPCIs.
► The MFPCIs are constructed by the intensity, Gabor and weighted entropy features.
► KDE algorithm tracks targets in MFPCIs can indirectly track many features.
► The tracking error in MFPCIs is smaller than that of tacking in IR images.