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Non-rigid target tracking based on 'flow-cut' in pair-wise frames with online hough forests Host Publication: 21st ACM international conference on Multimedia Pages Authors: T. Zhuo, Y. Zhang, P. Zhang, W. Huang and H. Sahli Publisher: ACM Publication Year: 2013
Abstract: 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.
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