This paper investigates a novel multi-target tracking algorithm for jointly estimating the number of multiple targets and their states from noisy measurements in the presence of data association uncertainty, target birth, clutter and missed detections. Probability hypothesis density (PHD) filter is a popular multi-target Bayes filter. But the standard PHD filter assumes that the target birth intensity is known or homogeneous, which usually results in inefficiency or false tracks in a cluttered scene. To solve this weakness, an iterative random sample consensus (I-RANSAC) algorithm with a sliding window is proposed to incrementally estimate the target birth intensity from uncertain measurements at each scan in time. More importantly, I-RANSAC is combined with PHD filter, which involves applying the PHD filter to eliminate clutter and noise, as well as to discriminate between survival and birth target originated measurements. Then birth targets originated measurements are employed to update the birth intensity by the I-RANSAC as the input of PHD filter. Experimental results prove that the proposed algorithm can improve number and state estimation of targets even in scenarios with intersections, occlusions, and birth targets born at arbitrary positions.
['Jingjing Wu', 'Ke Li', 'Qiuju Zhang', 'Wei An', 'Yi Jiang', 'Xueliang Ping', 'Peng Chen']
Iterative RANSAC based adaptive birth intensity estimation in GM-PHD filter for multi-target tracking