改进惯性权重的粒子群目标跟踪算法

粒子群优化算法中惯性权重的设置极其重要，直接影响算法性能。提出了一种改进的粒子群优化算法，并应用到目标跟踪领域，提高算法运算效率。首先，设置粒子群优化算法中的参数；其次，结合粒子优化率的概念记录粒子的不同状态，进而调节惯性权重，更新粒子的速度和位置；最后，对目标相似性函数进行优化，实现目标的准确定位。实验结果表明，该方法可以有效应对目标出现部分遮挡的跟踪难题，同时提高目标跟踪效率，具有较好的实时性。

Object tracking based on improved inertia weight of particle swarm optimization

The setting of inertia weight in the particle swarm optimization algorithm is crucial and has influence on the algorithm properties. In order to improve the operation efficiency of the algorithm, an improved particle swarm optimization is proposed and applied in the field of target tracking. First of all, the corresponding parameters of particle swarm optimization algorithm are initialized. Secondly, based on the introduction of the concept of particle optimization rate, inertia weight can be accurately adjusted according to the particle state in a timely manner, meanwhile, the speed and position of particles can be updated. Finally, there are optimization of objective similarity function and achievement of accurate positioning. Experimental results indicate that the method is well adapted to the situation when partial occlusion occurs in object tracking, and improves the operation efficiency with better real time.