Abstract
In this chapter we show how sparse constraints can be used to improve trajectories. We apply sparsity as a low rank constraint to trajectories via a robust coupling. We compute trajectories from an image sequence. Sparsity in trajectories is measured by matrix rank. We introduce a low rank constraint of linear complexity using random subsampling of the data and demonstrate that, by using a robust coupling with the low rank constraint, our approach outperforms baseline methods on general image sequences.
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Gibson, J., Marques, O. (2016). Robust Low Rank Trajectories. In: Optical Flow and Trajectory Estimation Methods. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-44941-8_4
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DOI: https://doi.org/10.1007/978-3-319-44941-8_4
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