基于多相机的多目标跟踪算法

多目标的稳定跟踪是计算机视觉领域的一个具有挑战性的问题. 本文提出了一种基于多相机的多目标定位跟踪算法.首先, 利用不同高度层上的标志物, 计算基于多层的不同视角间的单应性矩阵.然后, 利用码本模型对背景进行建模, 检测多个视角的前景似然信息.最后, 通过单应性变换获得多目标在不同高度层上的定位信息, 利用最短路径优化算法实现跟踪. 与其他算法相比, 本算法不需要计算多相机的隐消点, 降低了算法的复杂度, 提高了算法的准确性.采用最短路径优化算法, 提高了跟踪算法的效率. 实验结果表明, 本算法对遮挡具有很强的鲁棒性, 并且能够满足实时性要求.

A Multi-target Tracking Algorithm Based on Multiple Cameras

The reliable tracking of multi-targets is a challenging issue in computer vision. In this paper, we propose a novel multi-target localizing and tracking algorithm based on multiple cameras. Firstly, the view-to-view homographies are computed using several landmarks on different planes. Then, the foreground likelihood map in each view is obtained by using a codebook background modeling algorithm. Finally, we can localize multiple objects at multiple planes and perform tracking by shortest paths optimization. Compared with other popular methods, our proposed algorithm does not require computing the vanishing points of cameras. Therefore, it reduces the complexity and improves the accuracy simultaneously. Adopting the shortest path optimization algorithm can improve the tracking efficiency. The experimental results demonstrate that our method is robust to occlusion and also can achieve real-time performance.