346 research outputs found
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
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 Tracking with Correlation Filters using Selective Single Background Patch
Correlation filter plays a major role in improved tracking performance
compared to existing trackers. The tracker uses the adaptive correlation
response to predict the location of the target. Many varieties of correlation
trackers were proposed recently with high accuracy and frame rates. The paper
proposes a method to select a single background patch to have a better tracking
performance. The paper also contributes a variant of correlation filter by
modifying the filter with image restoration filters. The approach is validated
using Object Tracking Benchmark sequences.Comment: 5 pages, 3 figure
CREST: Convolutional Residual Learning for Visual Tracking
Discriminative correlation filters (DCFs) have been shown to perform
superiorly in visual tracking. They only need a small set of training samples
from the initial frame to generate an appearance model. However, existing DCFs
learn the filters separately from feature extraction, and update these filters
using a moving average operation with an empirical weight. These DCF trackers
hardly benefit from the end-to-end training. In this paper, we propose the
CREST algorithm to reformulate DCFs as a one-layer convolutional neural
network. Our method integrates feature extraction, response map generation as
well as model update into the neural networks for an end-to-end training. To
reduce model degradation during online update, we apply residual learning to
take appearance changes into account. Extensive experiments on the benchmark
datasets demonstrate that our CREST tracker performs favorably against
state-of-the-art trackers.Comment: ICCV 2017. Project page:
http://www.cs.cityu.edu.hk/~yibisong/iccv17/index.htm
Visual tracking with online assessment and improved sampling strategy
The kernelized correlation filter (KCF) is one of the most successful trackers in computer vision today. However its performance may be significantly degraded in a wide range of challenging conditions such as occlusion and out of view. For many applications, particularly safety critical applications (e.g. autonomous driving), it is of profound importance to have consistent and reliable performance during all the operation conditions. This paper addresses this issue of the KCF based trackers by the introduction of two novel modules, namely online assessment of response map, and a strategy of combining cyclically shifted sampling with random sampling in deep feature space. A method of online assessment of response map is proposed to evaluate the tracking performance by constructing a 2-D Gaussian estimation model. Then a strategy of combining cyclically shifted sampling with random sampling in deep feature space is presented to improve the tracking performance when the tracking performance is assessed to be unreliable based on the response map. Therefore, the module of online assessment can be regarded as the trigger for the second module. Experiments verify the tracking performance is significantly improved particularly in challenging conditions as demonstrated by both quantitative and qualitative comparisons of the proposed tracking algorithm with the state-of-the-art tracking algorithms on OTB-2013 and OTB-2015 datasets
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
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
Long-Term Visual Object Tracking Benchmark
We propose a new long video dataset (called Track Long and Prosper - TLP) and
benchmark for single object tracking. The dataset consists of 50 HD videos from
real world scenarios, encompassing a duration of over 400 minutes (676K
frames), making it more than 20 folds larger in average duration per sequence
and more than 8 folds larger in terms of total covered duration, as compared to
existing generic datasets for visual tracking. The proposed dataset paves a way
to suitably assess long term tracking performance and train better deep
learning architectures (avoiding/reducing augmentation, which may not reflect
real world behaviour). We benchmark the dataset on 17 state of the art trackers
and rank them according to tracking accuracy and run time speeds. We further
present thorough qualitative and quantitative evaluation highlighting the
importance of long term aspect of tracking. Our most interesting observations
are (a) existing short sequence benchmarks fail to bring out the inherent
differences in tracking algorithms which widen up while tracking on long
sequences and (b) the accuracy of trackers abruptly drops on challenging long
sequences, suggesting the potential need of research efforts in the direction
of long-term tracking.Comment: ACCV 2018 (Oral
Rate-Adaptive Neural Networks for Spatial Multiplexers
In resource-constrained environments, one can employ spatial multiplexing
cameras to acquire a small number of measurements of a scene, and perform
effective reconstruction or high-level inference using purely data-driven
neural networks. However, once trained, the measurement matrix and the network
are valid only for a single measurement rate (MR) chosen at training time. To
overcome this drawback, we answer the following question: How can we jointly
design the measurement operator and the reconstruction/inference network so
that the system can operate over a \textit{range} of MRs? To this end, we
present a novel training algorithm, for learning
\textbf{\textit{rate-adaptive}} networks. Using standard datasets, we
demonstrate that, when tested over a range of MRs, a rate-adaptive network can
provide high quality reconstruction over a the entire range, resulting in up to
about 15 dB improvement over previous methods, where the network is valid for
only one MR. We demonstrate the effectiveness of our approach for
sample-efficient object tracking where video frames are acquired at dynamically
varying MRs. We also extend this algorithm to learn the measurement operator in
conjunction with image recognition networks. Experiments on MNIST and CIFAR-10
confirm the applicability of our algorithm to different tasks
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