222 research outputs found
High Speed Tracking With A Fourier Domain Kernelized Correlation Filter
It is challenging to design a high speed tracking approach using l1-norm due
to its non-differentiability. In this paper, a new kernelized correlation
filter is introduced by leveraging the sparsity attribute of l1-norm based
regularization to design a high speed tracker. We combine the l1-norm and
l2-norm based regularizations in one Huber-type loss function, and then
formulate an optimization problem in the Fourier Domain for fast computation,
which enables the tracker to adaptively ignore the noisy features produced from
occlusion and illumination variation, while keep the advantages of l2-norm
based regression. This is achieved due to the attribute of Convolution Theorem
that the correlation in spatial domain corresponds to an element-wise product
in the Fourier domain, resulting in that the l1-norm optimization problem could
be decomposed into multiple sub-optimization spaces in the Fourier domain. But
the optimized variables in the Fourier domain are complex, which makes using
the l1-norm impossible if the real and imaginary parts of the variables cannot
be separated. However, our proposed optimization problem is formulated in such
a way that their real part and imaginary parts are indeed well separated. As
such, the proposed optimization problem can be solved efficiently to obtain
their optimal values independently with closed-form solutions. Extensive
experiments on two large benchmark datasets demonstrate that the proposed
tracking algorithm significantly improves the tracking accuracy of the original
kernelized correlation filter (KCF) while with little sacrifice on tracking
speed. Moreover, it outperforms the state-of-the-art approaches in terms of
accuracy, efficiency, and robustness
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
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS)
Object detection and tracking benchmark in industry based on improved correlation filter
Real-time object detection and tracking have shown to be the basis of
intelligent production for industrial 4.0 applications. It is a challenging
task because of various distorted data in complex industrial setting. The
correlation filter (CF) has been used to trade off the low-cost computation and
high performance. However, traditional CF training strategy can not get
satisfied performance for the various industrial data; because the simple
sampling(bagging) during training process will not find the exact solutions in
a data space with a large diversity. In this paper, we propose
Dijkstra-distance based correlation filters (DBCF), which establishes a new
learning framework that embeds distribution-related constraints into the
multi-channel correlation filters (MCCF). DBCF is able to handle the huge
variations existing in the industrial data by improving those constraints based
on the shortest path among all solutions. To evaluate DBCF, we build a new
dataset as the benchmark for industrial 4.0 application. Extensive experiments
demonstrate that DBCF produces high performance and exceeds the
state-of-the-art methods. The dataset and source code can be found at
https://github.com/bczhangbczhan
Object Tracking in Hyperspectral Videos with Convolutional Features and Kernelized Correlation Filter
Target tracking in hyperspectral videos is a new research topic. In this
paper, a novel method based on convolutional network and Kernelized Correlation
Filter (KCF) framework is presented for tracking objects of interest in
hyperspectral videos. We extract a set of normalized three-dimensional cubes
from the target region as fixed convolution filters which contain spectral
information surrounding a target. The feature maps generated by convolutional
operations are combined to form a three-dimensional representation of an
object, thereby providing effective encoding of local spectral-spatial
information. We show that a simple two-layer convolutional networks is
sufficient to learn robust representations without the need of offline training
with a large dataset. In the tracking step, KCF is adopted to distinguish
targets from neighboring environment. Experimental results demonstrate that the
proposed method performs well on sample hyperspectral videos, and outperforms
several state-of-the-art methods tested on grayscale and color videos in the
same scene.Comment: Accepted by ICSM 201
Depth Masked Discriminative Correlation Filter
Depth information provides a strong cue for occlusion detection and handling,
but has been largely omitted in generic object tracking until recently due to
lack of suitable benchmark datasets and applications. In this work, we propose
a Depth Masked Discriminative Correlation Filter (DM-DCF) which adopts novel
depth segmentation based occlusion detection that stops correlation filter
updating and depth masking which adaptively adjusts the spatial support for
correlation filter. In Princeton RGBD Tracking Benchmark, our DM-DCF is among
the state-of-the-art in overall ranking and the winner on multiple categories.
Moreover, since it is based on DCF, ``DM-DCF`` runs an order of magnitude
faster than its competitors making it suitable for time constrained
applications.Comment: 6 pages, accepted to ICPR 2018. \copyright 2018 IEE
Learning Spatial-Aware Regressions for Visual Tracking
In this paper, we analyze the spatial information of deep features, and
propose two complementary regressions for robust visual tracking. First, we
propose a kernelized ridge regression model wherein the kernel value is defined
as the weighted sum of similarity scores of all pairs of patches between two
samples. We show that this model can be formulated as a neural network and thus
can be efficiently solved. Second, we propose a fully convolutional neural
network with spatially regularized kernels, through which the filter kernel
corresponding to each output channel is forced to focus on a specific region of
the target. Distance transform pooling is further exploited to determine the
effectiveness of each output channel of the convolution layer. The outputs from
the kernelized ridge regression model and the fully convolutional neural
network are combined to obtain the ultimate response. Experimental results on
two benchmark datasets validate the effectiveness of the proposed method.Comment: To appear in CVPR201
Robust event-stream pattern tracking based on correlative filter
Object tracking based on retina-inspired and event-based dynamic vision
sensor (DVS) is challenging for the noise events, rapid change of event-stream
shape, chaos of complex background textures, and occlusion. To address these
challenges, this paper presents a robust event-stream pattern tracking method
based on correlative filter mechanism. In the proposed method, rate coding is
used to encode the event-stream object in each segment. Feature representations
from hierarchical convolutional layers of a deep convolutional neural network
(CNN) are used to represent the appearance of the rate encoded event-stream
object. The results prove that our method not only achieves good tracking
performance in many complicated scenes with noise events, complex background
textures, occlusion, and intersected trajectories, but also is robust to
variable scale, variable pose, and non-rigid deformations. In addition, this
correlative filter based event-stream tracking has the advantage of high speed.
The proposed approach will promote the potential applications of these
event-based vision sensors in self-driving, robots and many other high-speed
scenes
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
Discriminant Correlation Filters (DCF) based methods now become a kind of
dominant approach to online object tracking. The features used in these
methods, however, are either based on hand-crafted features like HoGs, or
convolutional features trained independently from other tasks like image
classification. In this work, we present an end-to-end lightweight network
architecture, namely DCFNet, to learn the convolutional features and perform
the correlation tracking process simultaneously. Specifically, we treat DCF as
a special correlation filter layer added in a Siamese network, and carefully
derive the backpropagation through it by defining the network output as the
probability heatmap of object location. Since the derivation is still carried
out in Fourier frequency domain, the efficiency property of DCF is preserved.
This enables our tracker to run at more than 60 FPS during test time, while
achieving a significant accuracy gain compared with KCF using HoGs. Extensive
evaluations on OTB-2013, OTB-2015, and VOT2015 benchmarks demonstrate that the
proposed DCFNet tracker is competitive with several state-of-the-art trackers,
while being more compact and much faster.Comment: 5 pages, 4 figure
Learning Rotation for Kernel Correlation Filter
Kernel Correlation Filters have shown a very promising scheme for visual
tracking in terms of speed and accuracy on several benchmarks. However it
suffers from problems that affect its performance like occlusion, rotation and
scale change. This paper tries to tackle the problem of rotation by
reformulating the optimization problem for learning the correlation filter.
This modification (RKCF) includes learning rotation filter that utilizes
circulant structure of HOG feature to guesstimate rotation from one frame to
another and enhance the detection of KCF. Hence it gains boost in overall
accuracy in many of OBT50 detest videos with minimal additional computation.Comment: 6 pages, 11 figures, tracking, CVPR, Correlation Filters,KCF, visual
object trackin
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