Non-rigid target tracking based on 'flow-cut' in pair-wise frames with online hough forests

In conventional online learning based tracking studies, fixedshape appearance modeling is often incorporated for training samples generation, as it is simple and convenient to be applied. However, for more general non-rigid and articulated object, this strategy may regard some background areas as foreground, which is likely to deteriorate the learning process. Recently published works utilize more than one patches to represent non-rigid object with foreground object segmentation, but most of these segmentation for target representation are performed only in single frame manner. Since the motion information between the consecutive frames was not considered by these approaches, when the backgrounds are similar to the target, accurate segmentation is hard to be achieved. In this work, we propose a novel model for non-rigid object segmentation by incorporating consecutive gradients flow between pair-wise frames into a Gibbs energy function. With help from motion information, the irregular target areas can be segmented more accurately during precise boundary convergence. The proposed segmentation model is incorporated into a semi-supervised online tracking framework for training samples generation. We test the proposed tracking on challenging videos involving heavy intrinsic variations and occlusions. As a result, the experiments demonstrate a significant improvement in tracking accuracy and robustness in comparison with other state-of-art tracking works.