21,837 research outputs found
Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System
The paper focuses on the problem of vision-based obstacle detection and
tracking for unmanned aerial vehicle navigation. A real-time object
localization and tracking strategy from monocular image sequences is developed
by effectively integrating the object detection and tracking into a dynamic
Kalman model. At the detection stage, the object of interest is automatically
detected and localized from a saliency map computed via the image background
connectivity cue at each frame; at the tracking stage, a Kalman filter is
employed to provide a coarse prediction of the object state, which is further
refined via a local detector incorporating the saliency map and the temporal
information between two consecutive frames. Compared to existing methods, the
proposed approach does not require any manual initialization for tracking, runs
much faster than the state-of-the-art trackers of its kind, and achieves
competitive tracking performance on a large number of image sequences.
Extensive experiments demonstrate the effectiveness and superior performance of
the proposed approach.Comment: 8 pages, 7 figure
On the Relations of Correlation Filter Based Trackers and Struck
In recent years, two types of trackers, namely correlation filter based
tracker (CF tracker) and structured output tracker (Struck), have exhibited the
state-of-the-art performance. However, there seems to be lack of analytic work
on their relations in the computer vision community. In this paper, we
investigate two state-of-the-art CF trackers, i.e., spatial regularization
discriminative correlation filter (SRDCF) and correlation filter with limited
boundaries (CFLB), and Struck, and reveal their relations. Specifically, after
extending the CFLB to its multiple channel version we prove the relation
between SRDCF and CFLB on the condition that the spatial regularization factor
of SRDCF is replaced by the masking matrix of CFLB. We also prove the
asymptotical approximate relation between SRDCF and Struck on the conditions
that the spatial regularization factor of SRDCF is replaced by an indicator
function of object bounding box, the weights of SRDCF in its loss item are
replaced by those of Struck, the linear kernel is employed by Struck, and the
search region tends to infinity. Extensive experiments on public benchmarks
OTB50 and OTB100 are conducted to verify our theoretical results. Moreover, we
explain how detailed differences among SRDCF, CFLB, and Struck would give rise
to slightly different performances on visual sequence
Spectral Filter Tracking
Visual object tracking is a challenging computer vision task with numerous
real-world applications. Here we propose a simple but efficient Spectral Filter
Tracking (SFT)method. To characterize rotational and translation invariance of
tracking targets, the candidate image region is models as a pixelwise grid
graph. Instead of the conventional graph matching, we convert the tracking into
a plain least square regression problem to estimate the best center coordinate
of the target. But different from the holistic regression of correlation filter
based methods, SFT can operate on localized surrounding regions of each pixel
(i.e.,vertex) by using spectral graph filters, which thus is more robust to
resist local variations and cluttered background.To bypass the eigenvalue
decomposition problem of the graph Laplacian matrix L, we parameterize spectral
graph filters as the polynomial of L by spectral graph theory, in which L k
exactly encodes a k-hop local neighborhood of each vertex. Finally, the filter
parameters (i.e., polynomial coefficients) as well as feature projecting
functions are jointly integrated into the regression model.Comment: 11page
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
Tracking Randomly Moving Objects on Edge Box Proposals
Most tracking-by-detection methods employ a local search window around the
predicted object location in the current frame assuming the previous location
is accurate, the trajectory is smooth, and the computational capacity permits a
search radius that can accommodate the maximum speed yet small enough to reduce
mismatches. These, however, may not be valid always, in particular for fast and
irregularly moving objects. Here, we present an object tracker that is not
limited to a local search window and has ability to probe efficiently the
entire frame. Our method generates a small number of "high-quality" proposals
by a novel instance-specific objectness measure and evaluates them against the
object model that can be adopted from an existing tracking-by-detection
approach as a core tracker. During the tracking process, we update the object
model concentrating on hard false-positives supplied by the proposals, which
help suppressing distractors caused by difficult background clutters, and learn
how to re-rank proposals according to the object model. Since we reduce
significantly the number of hypotheses the core tracker evaluates, we can use
richer object descriptors and stronger detector. Our method outperforms most
recent state-of-the-art trackers on popular tracking benchmarks, and provides
improved robustness for fast moving objects as well as for ultra low-frame-rate
videos
Effective Occlusion Handling for Fast Correlation Filter-based Trackers
Correlation filter-based trackers heavily suffer from the problem of multiple
peaks in their response maps incurred by occlusions. Moreover, the whole
tracking pipeline may break down due to the uncertainties brought by shifting
among peaks, which will further lead to the degraded correlation filter model.
To alleviate the drift problem caused by occlusions, we propose a novel scheme
to choose the specific filter model according to different scenarios.
Specifically, an effective measurement function is designed to evaluate the
quality of filter response. A sophisticated strategy is employed to judge
whether occlusions occur, and then decide how to update the filter models. In
addition, we take advantage of both log-polar method and pyramid-like approach
to estimate the best scale of the target. We evaluate our proposed approach on
VOT2018 challenge and OTB100 dataset, whose experimental result shows that the
proposed tracker achieves the promising performance compared against the
state-of-the-art trackers
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
Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking
Most thermal infrared (TIR) tracking methods are discriminative, treating the
tracking problem as a classification task. However, the objective of the
classifier (label prediction) is not coupled to the objective of the tracker
(location estimation). The classification task focuses on the between-class
difference of the arbitrary objects, while the tracking task mainly deals with
the within-class difference of the same objects. In this paper, we cast the TIR
tracking problem as a similarity verification task, which is coupled well to
the objective of the tracking task. We propose a TIR tracker via a Hierarchical
Spatial-aware Siamese Convolutional Neural Network (CNN), named HSSNet. To
obtain both spatial and semantic features of the TIR object, we design a
Siamese CNN that coalesces the multiple hierarchical convolutional layers.
Then, we propose a spatial-aware network to enhance the discriminative ability
of the coalesced hierarchical feature. Subsequently, we train this network end
to end on a large visible video detection dataset to learn the similarity
between paired objects before we transfer the network into the TIR domain.
Next, this pre-trained Siamese network is used to evaluate the similarity
between the target template and target candidates. Finally, we locate the
candidate that is most similar to the tracked target. Extensive experimental
results on the benchmarks VOT-TIR 2015 and VOT-TIR 2016 show that our proposed
method achieves favourable performance compared to the state-of-the-art
methods.Comment: 20 pages, 7 figure
Real-time quantitative Schlieren imaging by fast Fourier demodulation of a checkered backdrop
A quantitative synthetic Schlieren imaging (SSI) method based on fast Fourier
demodulation is presented. Instead of a random dot pattern (as usually employed
in SSI), a 2D periodic pattern (such as a checkerboard) is used as a backdrop
to the refractive object of interest. The range of validity and accuracy of
this "Fast Checkerboard Demodulation" (FCD) method are assessed using both
synthetic data and experimental recordings of patterns optically distorted by
small waves on a water surface. It is found that the FCD method is at least as
accurate as sophisticated, multi-stage, digital image correlation (DIC) or
optical flow (OF) techniques used with random dot patterns, and it is
significantly faster. Efficient, fully vectorized, implementations of both the
FCD and DIC/OF schemes developed for this study are made available as Matlab
scripts.Comment: 21 pages, 7 figures, 1 appendi
Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation
Aerial tracking, which has exhibited its omnipresent dedication and splendid
performance, is one of the most active applications in the remote sensing
field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system,
equipped with a visual tracking approach, has been widely used in aviation,
navigation, agriculture,transportation, and public security, etc. As is
mentioned above, the UAV-based aerial tracking platform has been gradually
developed from research to practical application stage, reaching one of the
main aerial remote sensing technologies in the future. However, due to the
real-world onerous situations, e.g., harsh external challenges, the vibration
of the UAV mechanical structure (especially under strong wind conditions), the
maneuvering flight in complex environment, and the limited computation
resources onboard, accuracy, robustness, and high efficiency are all crucial
for the onboard tracking methods. Recently, the discriminative correlation
filter (DCF)-based trackers have stood out for their high computational
efficiency and appealing robustness on a single CPU, and have flourished in the
UAV visual tracking community. In this work, the basic framework of the
DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art
DCF-based trackers are orderly summarized according to their innovations for
solving various issues. Besides, exhaustive and quantitative experiments have
been extended on various prevailing UAV tracking benchmarks, i.e., UAV123,
UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903
frames in total. The experiments show the performance, verify the feasibility,
and demonstrate the current challenges of DCF-based trackers onboard UAV
tracking.Comment: 28 pages, 10 figures, submitted to GRS
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