摘要
提出一种单目固定场景下,基于贝叶斯框架的数目可变的多目标实时跟踪方法。通过两个阶段实现对目标的有效跟踪:第一阶段为自动初始化,通过背景建模对视频序列进行检测,并实时提取目标的空间与颜色分布特征;第二阶段为运用粒子滤波器对目标进行跟踪与标定,通过目标间的特征匹配对对应矩阵进行实时更新,判断目标的数量变化情况及其发生概率。本文通过对道路监控视频序列中的车辆进行了跟踪仿真实验,验证了该方法的有效性和可靠性。
We propose an approach based on Bayesian framework for real-time tracking variable number of objects using fixed camera. The approach is performed at detection level and tracking level. At the detection level, a background-building arithmetic is used to extract the spatial and color distribution of objects in a complex circumstance. At the tracking level, we used particle fil- ter to track and tO label objects. To analyze the occurrences and probabilities of events as continu- ation, birth and death, we update the correspondence matrix by matching features of objects. We experiment the proposed approach on video sequences and verify the effectiveness and availability of the method.
出处
《中国传媒大学学报（自然科学版）》
2009年第2期17-20,共4页
Journal of Communication University of China：Science and Technology
基金
国家自然科学基金项目(60572041)资助
关键词
多目标跟踪
背景建模
粒子滤波器
对应矩阵
multi-target tracking
background building
particle filter
correspondence matrix
