摘要
针对无重叠视域中难以将运动目标与时空因素发生关联或关联后难以求解问题,提出了采用最优路径的数据关联算法并用离散蚁群算法进行了求解。算法首先利用贝叶斯网络,将目标外观匹配相似度、空间约束和时间约束三者融合,把数据关联问题转换为网络中最优路径的选择问题;其次,把路径间样本对的平均相似度设为评价函数,评价函数取最大值时的路径就是最优路径;最后,根据目标的出现在时间和空间存在离散性的特点,用离散粒子群算法求解最优路径,并用粒子编码记录目标运动路径。本算法在由五个摄像机构成的网络中对运动目标进行跟踪仿真,结果表明能有效地求解多目标的最优路径集合,获取了目标在网络中的运动轨迹,实现了接力跟踪,具有良好的鲁棒性。
Moving object is very difficult to be associated with the time and space elements, and also difficult to solve the association. To solve the problems, a algorithm is proposed which take optimal path set＇s data association algorithm, and the discrete particle swarm algorithm is brought to the solution. Firstly, the algorithm fuses the object＇s appearance match similarity, time and space constraint by Bayesian network net, and then transforms the data association problem into the optimal path choice in the network. Secondly, the average sample pairs＇ similarity between paths is set as evaluation function, and when its value is the maximum, the path is optimal. Finally, the emergence of target is discrete on time and space elements, so the discrete particle swarm algorithm is used to obtain the optimal path, and the target＇s moving path is recorded by particle encode. The algorithm makes a tracking target simulation in the networks composed of five cameras The results show that it can effectively solve the multiple target optimal path set, get the target＇s movement in the network, realize the relay track, and have good robustness
出处
《光电工程》
CAS
CSCD
北大核心
2014年第4期15-20,共6页
Opto-Electronic Engineering
基金
国家自然科学基金项目(61202290
61370173)
浙江省自然科学基金项目(LY12F02012)
关键词
无重叠视域
数据关联
离散粒子群算法
贝叶斯网络
non-overlapping
data association
discrete particle swarm algorithm
Bayesian networks
