无重叠视场的摄像机网络路径估计

Zhanli Li, Junchao Wang, Jiaying Chen
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引用次数: 1

摘要

近年来,场景监控技术应用广泛,广泛应用于安防监控领域,已成为安防监控的重要手段之一。本文给出了一种方法,在单个区域内,首先用计算复杂度较低的卡尔曼滤波代替计算量较大的TLD跟踪模块来获取目标轨迹。因此,在非重叠区域,提出了一种高斯均值互相关函数方法来估计摄像机之间的拓扑节点,为大规模场景下的连续跟踪提供了稳定的摄像机相关关系。结果表明,单区域视图下的目标跟踪效果良好,多区域视图下摄像机之间的拓扑关系估计也具有较好的抗干扰性,可行。
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Estimating Path in camera network with non-overlapping FOVs
In recent years, wide range of scene surveillance technology, widely used in the field of security monitoring, has become one of the important measure of security monitoring. The paper gives a method, in a single region, first use a low computational complexity of Kalman filter to replace the large computing TLD tracking module to obtain targets trajectories. Thus, in the non-overlapping regions, a Gaussian and mean cross-correlation function method is proposed to estimate the topological nodes between the cameras, which provides a stable camera correlation relationship for the continuous tracking in large scale scenes. The results show that the target tracking under the single region has good effect, and the topological relationship estimation between the cameras also has better anti-interference in the multi-region views, and feasible.
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