针对粒子群优化算法应用在目标跟踪时，其惯性权重调节机制的局限性，提出了改进的粒子群优化目标跟踪方法。首先，对目标及粒子群算法中相应参数进行初始化; 接着，引入粒子进化率的概念，对惯性权重调节机制进行改进，根据每代每个粒子的不同状态及时调整惯性权重; 然后，在更新粒子的速度和位置的同时，更新个体最优解和全局最优解，进行下一次迭代; 最后，比较粒子的适应度，选择相似性函数值最大的区域为目标。实验结果表明，该方法与使用自适应惯性权重调节机制的粒子群优化目标跟踪方法相比，减少了获取相同适应度所需的迭代次数，运算效率提高了42.9%。实现了目标在相似性函数出现“多峰”情况下的准确定位，对目标出现部分遮挡的情况具有很好的适应性。

To overcome the limitations of inertia weight adjustment mechanism when the particle swarm optimization algorithm is applied to object tracking, an improved particle swarm optimization object tracking algorithm is proposed. Firstly, the object and the parameters in particle swarm optimization algorithm are initialized. Secondly, the inertia weight adjustment mechanism is improved by using the evolution rate of particle, and the inertia weight is achieved by taking the conditions of different particles in each generation into consideration. Then the speed, the position, the individual optimum and the global optimum of the particles are updated simultaneously while the next iteration is proceeding. Finally, the area which has the largest similarity function value is defined as the object by comparing the fitness value of each particle with the others. Experimental results indicate that the method reduces the iterations to obtain the same fitness value, and improves the operation efficiency by 42.9% in comparison with the particle swarm optimization object tracking method which uses self-adapted inertia weight adjustment mechanism. The accurate positioning of the object is achieved in the case of the similarity function presenting “multimodal”, and the method is well adapted to the situation when partial occlusion occurs in object tracking.