52,274 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
Deep Motion Features for Visual Tracking
Robust visual tracking is a challenging computer vision problem, with many
real-world applications. Most existing approaches employ hand-crafted
appearance features, such as HOG or Color Names. Recently, deep RGB features
extracted from convolutional neural networks have been successfully applied for
tracking. Despite their success, these features only capture appearance
information. On the other hand, motion cues provide discriminative and
complementary information that can improve tracking performance. Contrary to
visual tracking, deep motion features have been successfully applied for action
recognition and video classification tasks. Typically, the motion features are
learned by training a CNN on optical flow images extracted from large amounts
of labeled videos.
This paper presents an investigation of the impact of deep motion features in
a tracking-by-detection framework. We further show that hand-crafted, deep RGB,
and deep motion features contain complementary information. To the best of our
knowledge, we are the first to propose fusing appearance information with deep
motion features for visual tracking. Comprehensive experiments clearly suggest
that our fusion approach with deep motion features outperforms standard methods
relying on appearance information alone.Comment: ICPR 2016. Best paper award in the "Computer Vision and Robot Vision"
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