Calderara Simone, Cucchiara Rita, Prati Andrea
Dipartimento di Ingegneria dell'Informazione, University of Modena and Reggio Emilia, Via Vignolese, 905, 41100 Modena-Italy.
IEEE Trans Pattern Anal Mach Intell. 2008 Feb;30(2):354-60. doi: 10.1109/TPAMI.2007.70814.
This paper presents a novel and robust approach to consistent labeling for people surveillance in multi-camera systems. A general framework scalable to any number of cameras with overlapped views is devised. An off-line training process automatically computes ground-plane homography and recovers epipolar geometry. When a new object is detected in any one camera, hypotheses for potential matching objects in the other cameras are established. Each of the hypotheses is evaluated using a prior and likelihood value. The prior accounts for the positions of the potential matching objects, while the likelihood is computed by warping the vertical axis of the new object on the field of view of the other cameras and measuring the amount of match. In the likelihood, two contributions (forward and backward) are considered so as to correctly handle the case of groups of people merged into single objects. Eventually, a maximum-a-posteriori approach estimates the best label assignment for the new object. Comparisons with other methods based on homography and extensive outdoor experiments demonstrate that the proposed approach is accurate and robust in coping with segmentation errors and in disambiguating groups.
本文提出了一种新颖且强大的方法，用于多摄像机系统中的人员监控一致性标注。设计了一个可扩展到任意数量具有重叠视图摄像机的通用框架。离线训练过程自动计算地面平面单应性并恢复极线几何。当在任何一台摄像机中检测到新物体时，会在其他摄像机中建立潜在匹配物体的假设。每个假设使用先验值和似然值进行评估。先验值考虑潜在匹配物体的位置，而似然值通过在其他摄像机的视场上对新物体的垂直轴进行扭曲并测量匹配量来计算。在似然值计算中，考虑了两个因素（正向和反向），以便正确处理人群合并为单个物体的情况。最终，最大后验方法估计新物体的最佳标签分配。与基于单应性的其他方法的比较以及广泛的户外实验表明，所提出的方法在应对分割错误和消除群体歧义方面准确且稳健。
