5,936 research outputs found
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
With efficient appearance learning models, Discriminative Correlation Filter
(DCF) has been proven to be very successful in recent video object tracking
benchmarks and competitions. However, the existing DCF paradigm suffers from
two major issues, i.e., spatial boundary effect and temporal filter
degradation. To mitigate these challenges, we propose a new DCF-based tracking
method. The key innovations of the proposed method include adaptive spatial
feature selection and temporal consistent constraints, with which the new
tracker enables joint spatial-temporal filter learning in a lower dimensional
discriminative manifold. More specifically, we apply structured spatial
sparsity constraints to multi-channel filers. Consequently, the process of
learning spatial filters can be approximated by the lasso regularisation. To
encourage temporal consistency, the filter model is restricted to lie around
its historical value and updated locally to preserve the global structure in
the manifold. Last, a unified optimisation framework is proposed to jointly
select temporal consistency preserving spatial features and learn
discriminative filters with the augmented Lagrangian method. Qualitative and
quantitative evaluations have been conducted on a number of well-known
benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and
VOT2018. The experimental results demonstrate the superiority of the proposed
method over the state-of-the-art approaches
A new fractional derivative involving the normalized sinc function without singular kernel
In this paper, a new fractional derivative involving the normalized sinc
function without singular kernel is proposed. The Laplace transform is used to
find the analytical solution of the anomalous heat-diffusion problems. The
comparative results between classical and fractional-order operators are
presented. The results are significant in the analysis of one-dimensional
anomalous heat-transfer problems.Comment: Keywords: Fractional derivative, anomalous heat diffusion, integral
transform, analytical solutio
Study of the weak annihilation contributions in charmless decays
In this paper, in order to probe the spectator-scattering and weak
annihilation contributions in charmless (where stands for a
light vector meson) decays, we perform the -analyses for the end-point
parameters within the QCD factorization framework, under the constraints from
the measured , , and
decays. The fitted results indicate that the end-point
parameters in the factorizable and nonfactorizable annihilation topologies are
non-universal, which is also favored by the charmless and (where
stands for a light pseudo-scalar meson) decays observed in the previous
work. Moreover, the abnormal polarization fractions measured by the LHCb
collaboration can be reconciled through the weak annihilation corrections.
However, the branching ratio of decay exhibits a
tension between the data and theoretical result, which dominates the
contributions to in the fits. Using the fitted end-point
parameters, we update the theoretical results for the charmless
decays, which will be further tested by the LHCb and Belle-II experiments in
the near future.Comment: 31 pages, 4 figures, 6 table
Human motion retrieval based on freehand sketch
In this paper, we present an integrated framework of human motion retrieval based on freehand sketch. With some simple rules, the user can acquire a desired motion by sketching several key postures. To retrieve efficiently and accurately by sketch, the 3D postures are projected onto several 2D planes. The limb direction feature is proposed to represent the input sketch and the projected-postures. Furthermore, a novel index structure based on k-d tree is constructed to index the motions in the database, which speeds up the retrieval process. With our posture-by-posture retrieval algorithm, a continuous motion can be got directly or generated by using a pre-computed graph structure. What's more, our system provides an intuitive user interface. The experimental results demonstrate the effectiveness of our method. © 2014 John Wiley & Sons, Ltd
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