Robust Visual Tracking via Implicit Low-Rank Constraints and Structural
Color Histograms
release_3pb25zcxibe7rct4tv7ty7l7fy
by
Yi-Xuan Wang, Xiao-Jun Wu, Xue-Feng Zhu
2019
Abstract
With the guaranteed discrimination and efficiency of spatial appearance
model, Discriminative Correlation Filters (DCF-) based tracking methods have
achieved outstanding performance recently. However, the construction of
effective temporal appearance model is still challenging on account of filter
degeneration becomes a significant factor that causes tracking failures in the
DCF framework. To encourage temporal continuity and to explore the smooth
variation of target appearance, we propose to enhance low-rank structure of the
learned filters, which can be realized by constraining the successive filters
within a ℓ_2-norm ball. Moreover, we design a global descriptor,
structural color histograms, to provide complementary support to the final
response map, improving the stability and robustness to the DCF framework. The
experimental results on standard benchmarks demonstrate that our Implicit
Low-Rank Constraints and Structural Color Histograms (ILRCSCH) tracker
outperforms state-of-the-art methods.
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