Aiming at the problem that the traditional tracking algorithm is greatly affected by the confusion between background clutter and different target typesa multi-target tracking method using n-type sequential Monte Carlo PHD (SMC-PHD) filter is proposed. Firstlybased on the stochastic finite set theorya new extended probability hypothesis density (PHD) filter modelnamely n-type PhD filteris proposedand the updated PHD is derived from the probability generating function. Thenthe sequential Monte Carlo (SMC) method is used to generate random samples. Combined with N-type PHD filterN-type SMC-PHD filtering algorithm is proposed to reduce the confusion detection of different target types. Finallythe measurement driven SMC-PHD filter is used to distinguish the measured attributes by the gating methodso as to suppress the influence of clutter on the existing target tracking. Based on MATLAB simulation platformthe proposed algorithm is tested in MOT17 and VS-PETS' 2003 soccer video data sets. The results show that the average optimal sub mode allocation (OSPA) distance of the proposed method is significantly reducedand it has high reliability in the case of high clutter and more target types.