Visual Tracking by Affine Kernel Fitting Using Color and Object Boundary

Abstract

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. Moreover, a more accurately localized target facilitates safer updating of its reference color model, further enhancing the tracker's robustness. The improved tracking is demonstrated for several challenging image sequences.