5,591 research outputs found
Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking
We propose a new Group Feature Selection method for Discriminative
Correlation Filters (GFS-DCF) based visual object tracking. The key innovation
of the proposed method is to perform group feature selection across both
channel and spatial dimensions, thus to pinpoint the structural relevance of
multi-channel features to the filtering system. In contrast to the widely used
spatial regularisation or feature selection methods, to the best of our
knowledge, this is the first time that channel selection has been advocated for
DCF-based tracking. We demonstrate that our GFS-DCF method is able to
significantly improve the performance of a DCF tracker equipped with deep
neural network features. In addition, our GFS-DCF enables joint feature
selection and filter learning, achieving enhanced discrimination and
interpretability of the learned filters.
To further improve the performance, we adaptively integrate historical
information by constraining filters to be smooth across temporal frames, using
an efficient low-rank approximation. By design, specific
temporal-spatial-channel configurations are dynamically learned in the tracking
process, highlighting the relevant features, and alleviating the performance
degrading impact of less discriminative representations and reducing
information redundancy. The experimental results obtained on OTB2013, OTB2015,
VOT2017, VOT2018 and TrackingNet demonstrate the merits of our GFS-DCF and its
superiority over the state-of-the-art trackers. The code is publicly available
at https://github.com/XU-TIANYANG/GFS-DCF
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
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigating the
relationship between template matching and the recent popular correlation
filter based trackers (CFTs). Compared to the correlation operation in CFTs, a
sophisticated similarity metric termed "mutual buddies similarity" (MBS) is
proposed to exploit the relationship of multiple reciprocal nearest neighbors
for target matching. By doing so, our tracker obtains powerful discriminative
ability on distinguishing target and background as demonstrated by both
empirical and theoretical analyses. Besides, instead of utilizing single
template with the improper updating scheme in CFTs, we design a novel online
template updating strategy named "memory filtering" (MF), which aims to select
a certain amount of representative and reliable tracking results in history to
construct the current stable and expressive template set. This scheme is
beneficial for the proposed tracker to comprehensively "understand" the target
appearance variations, "recall" some stable results. Both qualitative and
quantitative evaluations on two benchmarks suggest that the proposed tracking
method performs favorably against some recently developed CFTs and other
competitive trackers.Comment: has been published on IEEE TI
Large Margin Object Tracking with Circulant Feature Maps
Structured output support vector machine (SVM) based tracking algorithms have
shown favorable performance recently. Nonetheless, the time-consuming candidate
sampling and complex optimization limit their real-time applications. In this
paper, we propose a novel large margin object tracking method which absorbs the
strong discriminative ability from structured output SVM and speeds up by the
correlation filter algorithm significantly. Secondly, a multimodal target
detection technique is proposed to improve the target localization precision
and prevent model drift introduced by similar objects or background noise.
Thirdly, we exploit the feedback from high-confidence tracking results to avoid
the model corruption problem. We implement two versions of the proposed tracker
with the representations from both conventional hand-crafted and deep
convolution neural networks (CNNs) based features to validate the strong
compatibility of the algorithm. The experimental results demonstrate that the
proposed tracker performs superiorly against several state-of-the-art
algorithms on the challenging benchmark sequences while runs at speed in excess
of 80 frames per second. The source code and experimental results will be made
publicly available
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