摘要
近年来,实时监控下多目标跟踪作为智能交通系统(Intelli-gent transportation system,ITS)的重要组成部分受到关注.传统多目标跟踪方法通常具有处理速度慢、容易对交叉行进车辆产生误匹配等问题.本文首先对基于贝叶斯规则的车辆视频复杂背景的建模及运动目标的检测进行研究,在此基础上提出一种基于Mean shift粒子滤波(Mean shift particle filter,MSPF)的多目标跟踪算法,首先对每一目标车辆在下一帧可能出现的范围进行预测,对单目标和多目标情况采用不同的检测策略,避免了全局搜索,提高了跟踪速度;通过构造基于最新观测信息的重要性密度函数,提高了MSPF算法在复杂背景情况下追踪部分遮挡及交叉车辆的准确性和鲁棒性.仿真实验结果验证了所提出算法的有效性.
Recently, real-time monitoring multi-target tracking as an important component of intelligent transportation system （ITS） has been paid much attention. The traditional multitarget tracking algorithm has problems that the processing speed is slow and the false matches may happen when vehicles cross. Firstly, the algorithm detects moving targets through modeling a complex background based on Bayesian rules, then introduces a multi-target tracking algorithm based on mean shift particle filter （MSPF）. Firstly, the algorithm predicts the extent possible by the use of MSPF for each vehicle in the next frame, uses different detection strategies for simple or multiple targets to avoid a global search and improve the tracking speed; by con- structing the importance density function based on the latest observations, the algorithm can achieve an accurate and robust tracking in the part of the block and cross-vehicle. Simulation results verify the proposed algorithm.
出处
《自动化学报》
EI
CSCD
北大核心
2012年第1期139-144,共6页
Acta Automatica Sinica
基金
辽宁省自然基金项目(20102123)
辽宁"百千万人才工程"项目(2008921036)
南京邮电大学图像处理与图像通信江苏省重点实验室开放基金(LBEK2010003)
计算机软件新技术国家重点实验室开放基金(KFKT2011B11)资助~~
