Leichter Ido, Lindenbaum Michael, Rivlin Ehud
Technion - Israel Institute of Technology, Haifa, Israel.
IEEE Trans Pattern Anal Mach Intell. 2009 Jan;31(1):164-71. doi: 10.1109/TPAMI.2008.194.
Kernel-based trackers aggregate image features within the support of a kernel (a mask) regardless of their spatial structure. These trackers spatially fit the kernel (usually in location and in scale) such that a function of the aggregate is optimized. We propose a kernel-based visual tracker that exploits the constancy of color and the presence of color edges along the target boundary. The tracker estimates the best affinity of a spatially aligned pair of kernels, one of which is color-related and the other of which is object boundary-related. In a sense, this work extends previous kernel-based trackers by incorporating the object boundary cue into the tracking process and by allowing the kernels to be affinely transformed instead of only translated and isotropically scaled. These two extensions make for more precise target localization. A more accurately localized target also facilitates safer updating of its reference color model, further enhancing the tracker's robustness. The improved tracking is demonstrated for several challenging image sequences.
基于核的跟踪器在核（一个掩码）的支持范围内聚合图像特征，而不考虑它们的空间结构。这些跟踪器在空间上使核（通常在位置和尺度上）拟合，以便优化聚合函数。我们提出了一种基于核的视觉跟踪器，它利用颜色的恒定性以及目标边界处颜色边缘的存在。该跟踪器估计一对空间对齐的核的最佳亲和力，其中一个与颜色相关，另一个与对象边界相关。从某种意义上说，这项工作通过将对象边界线索纳入跟踪过程并允许核进行仿射变换而不仅仅是平移和各向同性缩放，扩展了先前基于核的跟踪器。这两个扩展使得目标定位更加精确。定位更精确的目标也有助于更安全地更新其参考颜色模型，进一步增强跟踪器的鲁棒性。针对几个具有挑战性的图像序列展示了改进后的跟踪效果。
