165 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
An Accelerated Correlation Filter Tracker
Recent visual object tracking methods have witnessed a continuous improvement
in the state-of-the-art with the development of efficient discriminative
correlation filters (DCF) and robust deep neural network features. Despite the
outstanding performance achieved by the above combination, existing advanced
trackers suffer from the burden of high computational complexity of the deep
feature extraction and online model learning. We propose an accelerated ADMM
optimisation method obtained by adding a momentum to the optimisation sequence
iterates, and by relaxing the impact of the error between DCF parameters and
their norm. The proposed optimisation method is applied to an innovative
formulation of the DCF design, which seeks the most discriminative spatially
regularised feature channels. A further speed up is achieved by an adaptive
initialisation of the filter optimisation process. The significantly increased
convergence of the DCF filter is demonstrated by establishing the optimisation
process equivalence with a continuous dynamical system for which the
convergence properties can readily be derived. The experimental results
obtained on several well-known benchmarking datasets demonstrate the efficiency
and robustness of the proposed ACFT method, with a tracking accuracy comparable
to the start-of-the-art trackers
Unsupervised Green Object Tracker (GOT) without Offline Pre-training
Supervised trackers trained on labeled data dominate the single object
tracking field for superior tracking accuracy. The labeling cost and the huge
computational complexity hinder their applications on edge devices.
Unsupervised learning methods have also been investigated to reduce the
labeling cost but their complexity remains high. Aiming at lightweight
high-performance tracking, feasibility without offline pre-training, and
algorithmic transparency, we propose a new single object tracking method,
called the green object tracker (GOT), in this work. GOT conducts an ensemble
of three prediction branches for robust box tracking: 1) a global object-based
correlator to predict the object location roughly, 2) a local patch-based
correlator to build temporal correlations of small spatial units, and 3) a
superpixel-based segmentator to exploit the spatial information of the target
frame. GOT offers competitive tracking accuracy with state-of-the-art
unsupervised trackers, which demand heavy offline pre-training, at a lower
computation cost. GOT has a tiny model size (<3k parameters) and low inference
complexity (around 58M FLOPs per frame). Since its inference complexity is
between 0.1%-10% of DL trackers, it can be easily deployed on mobile and edge
devices
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