17,333 research outputs found
Learning Target-oriented Dual Attention for Robust RGB-T Tracking
RGB-Thermal object tracking attempt to locate target object using
complementary visual and thermal infrared data. Existing RGB-T trackers fuse
different modalities by robust feature representation learning or adaptive
modal weighting. However, how to integrate dual attention mechanism for visual
tracking is still a subject that has not been studied yet. In this paper, we
propose two visual attention mechanisms for robust RGB-T object tracking.
Specifically, the local attention is implemented by exploiting the common
visual attention of RGB and thermal data to train deep classifiers. We also
introduce the global attention, which is a multi-modal target-driven attention
estimation network. It can provide global proposals for the classifier together
with local proposals extracted from previous tracking result. Extensive
experiments on two RGB-T benchmark datasets validated the effectiveness of our
proposed algorithm.Comment: Accepted by IEEE ICIP 201
Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition
This paper presents a comparative study of two different methods, which are
based on fusion and polar transformation of visual and thermal images. Here,
investigation is done to handle the challenges of face recognition, which
include pose variations, changes in facial expression, partial occlusions,
variations in illumination, rotation through different angles, change in scale
etc. To overcome these obstacles we have implemented and thoroughly examined
two different fusion techniques through rigorous experimentation. In the first
method log-polar transformation is applied to the fused images obtained after
fusion of visual and thermal images whereas in second method fusion is applied
on log-polar transformed individual visual and thermal images. After this step,
which is thus obtained in one form or another, Principal Component Analysis
(PCA) is applied to reduce dimension of the fused images. Log-polar transformed
images are capable of handling complicacies introduced by scaling and rotation.
The main objective of employing fusion is to produce a fused image that
provides more detailed and reliable information, which is capable to overcome
the drawbacks present in the individual visual and thermal face images.
Finally, those reduced fused images are classified using a multilayer
perceptron neural network. The database used for the experiments conducted here
is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database
benchmark thermal and visual face images. The second method has shown better
performance, which is 95.71% (maximum) and on an average 93.81% as correct
recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in
Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11
- 15, 201
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
In this paper, we present a Large-Scale and high-diversity general Thermal
InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an
evaluation dataset and a training dataset with a total of 1,400 TIR sequences
and more than 600K frames. We annotate the bounding box of objects in every
frame of all sequences and generate over 730K bounding boxes in total. To the
best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object
tracking benchmark to date. To evaluate a tracker on different attributes, we
define 4 scenario attributes and 12 challenge attributes in the evaluation
dataset. By releasing LSOTB-TIR, we encourage the community to develop deep
learning based TIR trackers and evaluate them fairly and comprehensively. We
evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of
baselines, and the results show that deep trackers achieve promising
performance. Furthermore, we re-train several representative deep trackers on
LSOTB-TIR, and their results demonstrate that the proposed training dataset
significantly improves the performance of deep TIR trackers. Codes and dataset
are available at https://github.com/QiaoLiuHit/LSOTB-TIR.Comment: accepted by ACM Mutlimedia Conference, 202
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