16,897 research outputs found
High-speed Tracking with Multi-kernel Correlation Filters
Correlation filter (CF) based trackers are currently ranked top in terms of
their performances. Nevertheless, only some of them, such as
KCF~\cite{henriques15} and MKCF~\cite{tangm15}, are able to exploit the
powerful discriminability of non-linear kernels. Although MKCF achieves more
powerful discriminability than KCF through introducing multi-kernel learning
(MKL) into KCF, its improvement over KCF is quite limited and its computational
burden increases significantly in comparison with KCF. In this paper, we will
introduce the MKL into KCF in a different way than MKCF. We reformulate the MKL
version of CF objective function with its upper bound, alleviating the negative
mutual interference of different kernels significantly. Our novel MKCF tracker,
MKCFup, outperforms KCF and MKCF with large margins and can still work at very
high fps. Extensive experiments on public datasets show that our method is
superior to state-of-the-art algorithms for target objects of small move at
very high speed.Comment: 10+3 pages, 12 figures, 1 table, accepted by CVPR2018. This version
corrects some typos, and supplements a proo
Kernel Cross-Correlator
Cross-correlator plays a significant role in many visual perception tasks,
such as object detection and tracking. Beyond the linear cross-correlator, this
paper proposes a kernel cross-correlator (KCC) that breaks traditional
limitations. First, by introducing the kernel trick, the KCC extends the linear
cross-correlation to non-linear space, which is more robust to signal noises
and distortions. Second, the connection to the existing works shows that KCC
provides a unified solution for correlation filters. Third, KCC is applicable
to any kernel function and is not limited to circulant structure on training
data, thus it is able to predict affine transformations with customized
properties. Last, by leveraging the fast Fourier transform (FFT), KCC
eliminates direct calculation of kernel vectors, thus achieves better
performance yet still with a reasonable computational cost. Comprehensive
experiments on visual tracking and human activity recognition using wearable
devices demonstrate its robustness, flexibility, and efficiency. The source
codes of both experiments are released at https://github.com/wang-chen/KCCComment: The Thirty-Second AAAI Conference on Artificial Intelligence
(AAAI-18
EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking
Computer vision technologies are very attractive for practical applications
running on embedded systems. For such an application, it is desirable for the
deployed algorithms to run in high-speed and require no offline training. To
develop a single-target tracking algorithm with these properties, we propose an
ensemble of the kernelized correlation filters (KCF), we call it EnKCF. A
committee of KCFs is specifically designed to address the variations in scale
and translation of moving objects. To guarantee a high-speed run-time
performance, we deploy each of KCFs in turn, instead of applying multiple KCFs
to each frame. To minimize any potential drifts between individual KCFs
transition, we developed a particle filter. Experimental results showed that
the performance of ours is, on average, 70.10% for precision at 20 pixels,
53.00% for success rate for the OTB100 data, and 54.50% and 40.2% for the
UAV123 data. Experimental results showed that our method is better than other
high-speed trackers over 5% on precision on 20 pixels and 10-20% on AUC on
average. Moreover, our implementation ran at 340 fps for the OTB100 and at 416
fps for the UAV123 dataset that is faster than DCF (292 fps) for the OTB100 and
KCF (292 fps) for the UAV123. To increase flexibility of the proposed EnKCF
running on various platforms, we also explored different levels of deep
convolutional features
Kernalised Multi-resolution Convnet for Visual Tracking
Visual tracking is intrinsically a temporal problem. Discriminative
Correlation Filters (DCF) have demonstrated excellent performance for
high-speed generic visual object tracking. Built upon their seminal work, there
has been a plethora of recent improvements relying on convolutional neural
network (CNN) pretrained on ImageNet as a feature extractor for visual
tracking. However, most of their works relying on ad hoc analysis to design the
weights for different layers either using boosting or hedging techniques as an
ensemble tracker. In this paper, we go beyond the conventional DCF framework
and propose a Kernalised Multi-resolution Convnet (KMC) formulation that
utilises hierarchical response maps to directly output the target movement.
When directly deployed the learnt network to predict the unseen challenging UAV
tracking dataset without any weight adjustment, the proposed model consistently
achieves excellent tracking performance. Moreover, the transfered
multi-reslution CNN renders it possible to be integrated into the RNN temporal
learning framework, therefore opening the door on the end-to-end temporal deep
learning (TDL) for visual tracking.Comment: CVPRW 201
Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling
It remains a huge challenge to design effective and efficient trackers under
complex scenarios, including occlusions, illumination changes and pose
variations. To cope with this problem, a promising solution is to integrate the
temporal consistency across consecutive frames and multiple feature cues in a
unified model. Motivated by this idea, we propose a novel correlation
filter-based tracker in this work, in which the temporal relatedness is
reconciled under a multi-task learning framework and the multiple feature cues
are modeled using a multi-view learning approach. We demonstrate the resulting
regression model can be efficiently learned by exploiting the structure of
blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm
is developed thereafter for efficient online tracking. Meanwhile, we
incorporate an adaptive scale estimation mechanism to strengthen the stability
of scale variation tracking. We implement our tracker using two types of
features and test it on two benchmark datasets. Experimental results
demonstrate the superiority of our proposed approach when compared with other
state-of-the-art trackers. project homepage
http://bmal.hust.edu.cn/project/KMF2JMTtracking.htmlComment: This paper has been accepted by IEEE Transactions on Circuits and
Systems for Video Technology. The MATLAB code of our method is available from
our project homepage http://bmal.hust.edu.cn/project/KMF2JMTtracking.htm
An Experimental Survey on Correlation Filter-based Tracking
Over these years, Correlation Filter-based Trackers (CFTs) have aroused
increasing interests in the field of visual object tracking, and have achieved
extremely compelling results in different competitions and benchmarks. In this
paper, our goal is to review the developments of CFTs with extensive
experimental results. 11 trackers are surveyed in our work, based on which a
general framework is summarized. Furthermore, we investigate different training
schemes for correlation filters, and also discuss various effective
improvements that have been made recently. Comprehensive experiments have been
conducted to evaluate the effectiveness and efficiency of the surveyed CFTs,
and comparisons have been made with other competing trackers. The experimental
results have shown that state-of-art performance, in terms of robustness, speed
and accuracy, can be achieved by several recent CFTs, such as MUSTer and SAMF.
We find that further improvements for correlation filter-based tracking can be
made on estimating scales, applying part-based tracking strategy and
cooperating with long-term tracking methods.Comment: 13 pages, 25 figure
High-Speed Tracking with Kernelized Correlation Filters
The core component of most modern trackers is a discriminative classifier,
tasked with distinguishing between the target and the surrounding environment.
To cope with natural image changes, this classifier is typically trained with
translated and scaled sample patches. Such sets of samples are riddled with
redundancies -- any overlapping pixels are constrained to be the same. Based on
this simple observation, we propose an analytic model for datasets of thousands
of translated patches. By showing that the resulting data matrix is circulant,
we can diagonalize it with the Discrete Fourier Transform, reducing both
storage and computation by several orders of magnitude. Interestingly, for
linear regression our formulation is equivalent to a correlation filter, used
by some of the fastest competitive trackers. For kernel regression, however, we
derive a new Kernelized Correlation Filter (KCF), that unlike other kernel
algorithms has the exact same complexity as its linear counterpart. Building on
it, we also propose a fast multi-channel extension of linear correlation
filters, via a linear kernel, which we call Dual Correlation Filter (DCF). Both
KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50
videos benchmark, despite running at hundreds of frames-per-second, and being
implemented in a few lines of code (Algorithm 1). To encourage further
developments, our tracking framework was made open-source
Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters
Hyperspectral imaging holds enormous potential to improve the
state-of-the-art in aerial vehicle tracking with low spatial and temporal
resolutions. Recently, adaptive multi-modal hyperspectral sensors have
attracted growing interest due to their ability to record extended data quickly
from aerial platforms. In this study, we apply popular concepts from
traditional object tracking, namely (1) Kernelized Correlation Filters (KCF)
and (2) Deep Convolutional Neural Network (CNN) features to aerial tracking in
hyperspectral domain. We propose the Deep Hyperspectral Kernelized Correlation
Filter based tracker (DeepHKCF) to efficiently track aerial vehicles using an
adaptive multi-modal hyperspectral sensor. We address low temporal resolution
by designing a single KCF-in-multiple Regions-of-Interest (ROIs) approach to
cover a reasonably large area. To increase the speed of deep convolutional
features extraction from multiple ROIs, we design an effective ROI mapping
strategy. The proposed tracker also provides flexibility to couple with the
more advanced correlation filter trackers. The DeepHKCF tracker performs
exceptionally well with deep features set up in a synthetic hyperspectral video
generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG)
software. Additionally, we generate a large, synthetic, single-channel dataset
using DIRSIG to perform vehicle classification in the Wide Area Motion Imagery
(WAMI) platform. This way, the high-fidelity of the DIRSIG software is proved
and a large scale aerial vehicle classification dataset is released to support
studies on vehicle detection and tracking in the WAMI platform
Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking
Being intensively studied, visual tracking has seen great recent advances in
either speed (e.g., with correlation filters) or accuracy (e.g., with deep
features). Real-time and high accuracy tracking algorithms, however, remain
scarce. In this paper we study the problem from a new perspective and present a
novel parallel tracking and verifying (PTAV) framework, by taking advantage of
the ubiquity of multi-thread techniques and borrowing from the success of
parallel tracking and mapping in visual SLAM. Our PTAV framework typically
consists of two components, a tracker T and a verifier V, working in parallel
on two separate threads. The tracker T aims to provide a super real-time
tracking inference and is expected to perform well most of the time; by
contrast, the verifier V checks the tracking results and corrects T when
needed. The key innovation is that, V does not work on every frame but only
upon the requests from T; on the other end, T may adjust the tracking according
to the feedback from V. With such collaboration, PTAV enjoys both the high
efficiency provided by T and the strong discriminative power by V. In our
extensive experiments on popular benchmarks including OTB2013, OTB2015, TC128
and UAV20L, PTAV achieves the best tracking accuracy among all real-time
trackers, and in fact performs even better than many deep learning based
solutions. Moreover, as a general framework, PTAV is very flexible and has
great rooms for improvement and generalization.Comment: 9 page
Part-based Visual Tracking via Structural Support Correlation Filter
Recently, part-based and support vector machines (SVM) based trackers have
shown favorable performance. Nonetheless, the time-consuming online training
and updating process limit their real-time applications. In order to better
deal with the partial occlusion issue and improve their efficiency, we propose
a novel part-based structural support correlation filter tracking method, which
absorbs the strong discriminative ability from SVM and the excellent property
of part-based tracking methods which is less sensitive to partial occlusion.
Then, our proposed model can learn the support correlation filter of each part
jointly by a star structure model, which preserves the spatial layout structure
among parts and tolerates outliers of parts. In addition, to mitigate the issue
of drift away from object further, we introduce inter-frame consistencies of
local parts into our model. Finally, in our model, we accurately estimate the
scale changes of object by the relative distance change among reliable parts.
The extensive empirical evaluations on three benchmark datasets: OTB2015,
TempleColor128 and VOT2015 demonstrate that the proposed method performs
superiorly against several state-of-the-art trackers in terms of tracking
accuracy, speed and robustness
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