1 research outputs found
Tracking Deformable Parts via Dynamic Conditional Random Fields
Despite the success of many advanced tracking methods in this area, tracking
targets with drastic variation of appearance such as deformation, view change
and partial occlusion in video sequences is still a challenge in practical
applications. In this letter, we take these serious tracking problems into
account simultaneously, proposing a dynamic graph based model to track object
and its deformable parts at multiple resolutions. The method introduces well
learned structural object detection models into object tracking applications as
prior knowledge to deal with deformation and view change. Meanwhile, it
explicitly formulates partial occlusion by integrating spatial potentials and
temporal potentials with an unparameterized occlusion handling mechanism in the
dynamic conditional random field framework. Empirical results demonstrate that
the method outperforms state-of-the-art trackers on different challenging video
sequences.Comment: 4 pages, 5 figures, the manuscript has been submitted to IEEE Signal
Processing Letter