To solve the problem of box particle redundancy in existing Box Particle Probability Hypothesis Density (BOX-PHD) filter, a new multi-target tracking algorithm based on box particle dividing PHD filter is proposed.Before the update stage of the target state estimation, each box particle obtained from prediction is divided into several sub-box particles to obtain the equivalent box particle subsets.Then, by using interval measurement, the weights of these box particle subsets are updated to estimate the state and number of the targets.The interval measurement information can be better used because the box particle is divided smaller.Also, the biased estimation caused by the insufficient compression of box particles can be effectively avoided.Simulation results show that the proposed method can effectively improve the target tracking performance.