Wen, ZhuomanZhuomanWenKuijper, ArjanArjanKuijperFraissinet-Tachet, MatthieuMatthieuFraissinet-TachetWang, YanjieYanjieWangLuo, JunJunLuo2022-03-052022-03-052017https://publica.fraunhofer.de/handle/publica/24960510.1007/s13369-017-2541-zBased onmutual information (MI), this paper proposes a systematic analysis of tracking a multi-plane object with multiple cameras. Firstly, a geometric model consisting of a piecewise planar object and multiple cameras is setup. Given an initial pose guess, the method seeks a pose update that maximizes the global MI of all the pairs of reference image and camera image. An object pose-dependent warp is proposed to ensure computation precision. Six variations of the proposed method are designed and tested. Mode 1, i.e., computing the 2nd-order Hessian of MI at each step as the object pose changes, leads to the highest convergence rates; Mode 2, i.e., computing the 1st-order Hessian of MI once at the beginning, occupies the least time (0.5-1.0 s). For objects with simple-textured planes, applying Gaussian blur first and then useMode 1 shall generate the highest convergence rate.enComputer visionCamera trackingImage registrationMutual information (MI)Nonlinear optimizationDigitized Workmulti-camera tracking006journal article