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Beyond Background-Aware Correlation Filters: Adaptive Context Modeling by Hand-Crafted and Deep RGB Features for Visual Tracking
[article]
2021
arXiv
pre-print
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Comprehensive experimental results demonstrate that the proposed adaptive method clearly outperforms the accuracy and robustness of visual target tracking compared to the state-of-the-art methods on the ...
On the other hand, the recent deep learning-based visual tracking methods have provided a competitive performance along with extensive computations. ...
Otherwise, the HOG context model was used to increase visual tracking speed. ...
arXiv:2004.02932v2
fatcat:dbjgzsequvcpxgq2qlilv6rmbi
Contrastive Learning of Semantic and Visual Representations for Text Tracking
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2112.14976v2 fatcat:fobodorg4ncwnisx6atyrxaxa4Other Versions
In this paper, we explore to robustly track video text with contrastive learning of semantic and visual representations. ...
Correspondingly, we present an end-to-end video text tracker with Semantic and Visual Representations(SVRep), which detects and tracks texts by exploiting the visual and semantic relationships between ...
In contrast to the visual features of appearance, semantic features are robust cues for matching and tracking text instances in a video sequence. ...
arXiv:2112.14976v3
fatcat:v676lcpk55cgbkexim22vrhqhq
Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking
[article]
2020
arXiv
pre-print
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tracking performance but also provide acceptable tracking speeds. ...
Comprehensive experimental results on the OTB-50, OTB-100, TC-128 and VOT-2018 visual tracking datasets demonstrate that the proposed visual tracking methods outperform the state-of-the-art methods, effectively ...
Due to the desired robustness and computational efficiency, DCF-based methods have extensively been used for visual tracking purposes. ...
arXiv:2004.02933v2
fatcat:p3sqisjjpvhbrcyzceknelefnm
A Review of Visual Trackers and Analysis of its Application to Mobile Robot
[article]
2019
arXiv
pre-print
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Then the structure of generative and discriminative methods is introduced, and summarized the feature descriptors, modeling methods, and learning methods which be used in tracker. ...
Later we reviewed and evaluated the state-of-the-art progress on discriminative trackers from three directions: correlation filter, deep learning and convolutional features. ...
The combined tracker speeds up to 80FPS. Since then, HOG and Color Names have become the standard of Hand-Crafted features in tracking algorithm. ...
arXiv:1910.09761v1
fatcat:h5lw5gy3svdexir2sioi4lgmom
Siamese Transformer Pyramid Networks for Real-Time UAV Tracking
[article]
2021
arXiv
pre-print
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Comprehensive experiments on both aerial and prevalent tracking benchmarks achieve competitive results while operating at high speed, demonstrating the effectiveness of SiamTPN. ...
Specifically, we exploit the inherent feature pyramid of a lightweight network (ShuffleNetV2) and reinforce it with a Transformer to construct a robust target-specific appearance model. ...
We set up three different tracking scenarios to validate the tracking speed, generalization ability and robustness of SiamTPN. ...
arXiv:2110.08822v1
fatcat:xmng5qlwy5favftmenfkijhxfu
PatchNet – Short-range Template Matching for Efficient Video Processing
[article]
2021
arXiv
pre-print
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We demonstrate its application on two tasks, video object detection and visual object tracking. ...
It learns the patchwise correlation features instead of pixel features. PatchNet is very compact, running at just 58MFLOPs, 5× simpler than MobileNetV2. ...
this problem. • For video object detection, patchNet achieves 4.9x speed-up over R-FCN ResNet-101 and 3.4x speed-up over EfficientDet-D0, with less than 1% mAP loss. • For visual object tracking, PatchNet ...
arXiv:2103.07371v1
fatcat:ejzywqowrjexfcrpibje7vxsvm
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2012.04872v1 fatcat:k6htgscscnez7lcx2uvsjvwxc4
In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals. ...
To train and evaluate our tracker, we introduce and release the first lesion tracking benchmark, consisting of 3891 lesion pairs from the public DeepLesion database. ...
Visualization examples for lesion tracking with multiple follow ups. We show lesion tracking using DLT with three follow-ups in Fig. 7 and Fig. 8 . ...
arXiv:2012.04872v2
fatcat:xg2gm4o7y5c4zpkd2xylziy664
Do not Lose the Details: Reinforced Representation Learning for High Performance Visual Tracking
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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This work presents a novel end-to-end trainable CNN model for high performance visual object tracking. ...
It learns both low-level fine-grained representations and a high-level semantic embedding space in a mutual reinforced way, and a multi-task learning strategy is proposed to perform the correlation analysis ...
to more robust visual tracking. ...
doi:10.24963/ijcai.2018/137
dblp:conf/ijcai/WangZXGHM18
fatcat:vuixvii3hfdw5pp5snso2kxaom
Deep Learning Trackers Review and Challenge
2019
Journal of Information Hiding and Privacy Protection
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. (4) The deep visual trackers using end-to-end networks usually perform better than the trackers merely using feature extraction networks. (5) For visual tracking, the most suitable network training method ...
H. (2015): Hierarchical convolutional features for visual tracking. Baek, M.; Han, B. (2016): Modeling and propagating cnns in a tree structure for visual tracking. arXiv:1608.07242. ...
Tracking speed Speed is also an important aspect for online visual tracking, which is mainly effected by model complexity and update frequency. ...
doi:10.32604/jihpp.2019.05938
fatcat:z2kq47sl25fz7fykzdhfozysge
Fast and Robust Visual Tracking with Few-Iteration Meta-Learning
2022
Sensors
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In addition, the object-tracking algorithms are also required to have robustness and real-time performance. ...
The primary goal of a meta learner based on the transformer is to learn the representations used by the classifier. ...
The tracking results of MDNet [37] and CREST [38] are improved by training a more robust feature initialization extractor using the meta-learning approach of MAML [39] . Wang et al. ...
doi:10.3390/s22155826
pmid:35957383
pmcid:PMC9370940
fatcat:ubpsovfpafau5jybql2w7jikqm
Attentive Deep Regression Networks for Real-Time Visual Face Tracking in Video Surveillance
[article]
2019
arXiv
pre-print
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Recent studies show that deep learning methods have a significant potential in object tracking tasks and adaptive feature selection methods can boost their performance. ...
Our method outperforms the state-of-the-art GOTURN and IVT trackers by very large margins and it achieves speeds that are very far beyond the requirements of real-time tracking. ...
By this way, while the unnecessary features will get suppressed, the useful ones will get pushed up.
C. ...
arXiv:1908.03812v1
fatcat:bba5qlu4kjhyzb5baynspth6ja
Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks
[article]
2020
arXiv
pre-print
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To the best of our knowledge, TS-RCN is the first end-to-end trainable two-stream visual tracking system, which makes full use of both appearance and motion features of the target. ...
The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised training data in offline ...
In contrast, our approach is an end-to-end trainable deep learning based visual tracking system, which can run up to 38.1 FPS with much higher tracking accuracy. ...
arXiv:2005.06536v1
fatcat:5mhnd5pb7zgupp4mzd72mjf5sm
Learning Policies for Adaptive Tracking with Deep Feature Cascades
[article]
2017
arXiv
pre-print
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Visual object tracking is a fundamental and time-critical vision task. ...
We train the agent offline in a reinforcement learning fashion, and further demonstrate that learning all deep layers (so as to provide good features for adaptive tracking) can lead to near real-time average ...
Additional support was provided by the Intel Science and Technology Center for Visual Cloud Systems (ISTC-VCS), Google, and Autel. ...
arXiv:1708.02973v2
fatcat:lcz7qxhk6nhizmow3bf2u2hi2q
BIT: Biologically Inspired Tracker
2016
IEEE Transactions on Image Processing
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low-level biologically inspired features for the target appearance and imitates an advanced learning mechanism (S2 units and C2 units) to combine generative and discriminative models for target location ...
Given the superior tracking performance of human visual system (HVS), an ideal design of biologically inspired model is expected to improve computer visual tracking. ...
BIT exploits fast Gabor approximation (FGA) to speed up low-level bio-inspired feature extraction (S1 units and C1 units) and fast Fourier transform (FFT) to speed up high-level bio-inspired learning and ...
doi:10.1109/tip.2016.2520358
pmid:26800541
fatcat:crwiqlfhtnbzdpjw5ui7754tme
DensSiam: End-to-End Densely-Siamese Network with Self-Attention Model for Object Tracking
[article]
2018
arXiv
pre-print
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Convolutional Siamese neural networks have been recently used to track objects using deep features. ...
Extensive experiments are performed on four tracking benchmarks: OTB2013 and OTB2015 for validation set; and VOT2015, VOT2016 and VOT2017 for testing set. ...
SINT [36] uses optical flow and formulates the visual object tracking as a verification problem within Siamese architecture, it has a better performance however, the speed dropped down from 86 to 4 frames ...
arXiv:1809.02714v1
fatcat:x3jpryp255am5jcxyyd25p6ql4
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