自适应的免疫粒子滤波车辆跟踪算法

Vehicle Tracking Algorithm Based on Adaptive Immune Particle Filter

针对真实场景中的车辆跟踪问题,提出一种改进的粒子滤波车辆跟踪算法.通过免疫重采样框架减少粒子退化,保证粒子滤波的有效性,并参照人工免疫算法的思想建立记忆库,使算法可较长时间地跟踪目标;利用背景权重直方图和分块判别机制减少因遮挡导致的跟踪偏离,同时在运动模型和抗体变异过程中加入自适应学习参数,提高算法的鲁棒性.实验结果表明,在光照变化、运动突变、目标遮挡等不同条件下,该算法具有稳定跟踪的能力,验证了算法的有效性.

Aiming at the problem of vehicle tracking in real scene,we proposed an improved particle filter for vehicle tracking algorithm.By using the framework of immune resampling,it reduced particle degeneracy and ensured the effectiveness of particle filter.Meanwhile,a memory base was established which refers to the idea of artificial immune algorithm,so that the algorithm could track targets for a long time.The background weights histogram and sub-block identification mechanisms were used to reduce occlusions which caused by off-tracking. Moreover,the adaptive learning parameters were added to the movement model and antibody mutation,which could improve the robustness of the algorithm.The experimental results show the algorithm has ability of stable tracking under the different conditions of illumination change,sudden movement or target occlusion,which verifies the validity of the proposed algorithm.