A precise approach to tracking dim-small targets using spectral fingerprint features

Abstract

A precise method for accurately tracking dim-small targets, based on spectral fingerprint is proposed where traditional full color tracking seems impossible. A fingerprint model is presented to adequately extract spectral features. By creating a multidimensional feature space and extending the limited RGB information to the hyperspectral information, the improved precise tracking model based on a nonparametric kernel density estimator is built using the probability histogram of spectral features. A layered particle filter algorithm for spectral tracking is presented to avoid the object jumping abruptly. Finally, experiments are conducted that show that the tracking algorithm with spectral fingerprint features is accurate, fast, and robust. It meets the needs of dim-small target tracking adequately.