Robust Visual Tracking via Implicit Low-Rank Constraints and Structural Color Histograms
This work addresses tracking failures in computer vision, offering incremental improvements to DCF-based methods.
The paper tackled the problem of filter degeneration in Discriminative Correlation Filters (DCF)-based visual tracking by enhancing low-rank structure and using structural color histograms, resulting in a tracker that outperforms state-of-the-art methods on standard benchmarks.
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 $\ell_2$-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.