A multi-target tracking algorithm combining PHD filter with adaptive detection of newborn targets is developed.

A novel birth intensity estimation approach is proposed to accurately and robustly determine the intensity of new targets.

A measurement classifying approach is proposed to remove errors from the measurement uncertainties.

A spatio-temporal filtering based on I-RANSAC is proposed to further eliminate errors of birth intensity from clutter.

The proposed tracker can improve number and state estimation of targets in complicated scenarios.