scholarly journals Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8100
Author(s):  
Bin Yu ◽  
Ming Tang ◽  
Guibo Zhu ◽  
Jinqiao Wang ◽  
Hanqing Lu
Keyword(s):  
Visual Tracking ◽  
Large Scale ◽  
State Of The Art ◽  
Estimation Method ◽  
Target Object ◽  
Reference Information ◽  
Bounding Box ◽  
The Mean ◽  
Modulation Vector ◽  
Initial Target

Bounding box estimation by overlap maximization has improved the state of the art of visual tracking significantly, yet the improvement in robustness and accuracy is restricted by the limited reference information, i.e., the initial target. In this paper, we present DCOM, a novel bounding box estimation method for visual tracking, based on distribution calibration and overlap maximization. We assume every dimension in the modulation vector follows a Gaussian distribution, so that the mean and the variance can borrow from those of similar targets in large-scale training datasets. As such, sufficient and reliable reference information can be obtained from the calibrated distribution, leading to a more robust and accurate target estimation. Additionally, an updating strategy for the modulation vector is proposed to adapt the variation of the target object. Our method can be built on top of off-the-shelf networks without finetuning and extra parameters. It yields state-of-the-art performance on three popular benchmarks, including GOT-10k, LaSOT, and NfS while running at around 40 FPS, confirming its effectiveness and efficiency.

scholarly journals Robust Scale Adaptive Visual Tracking with Correlation Filters

2018 ◽  
Vol 8 (11) ◽  
pp. 2037 ◽  
Author(s):  
Chunbao Li ◽  
Bo Yang
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Estimation Method ◽  
Color Naming ◽  
Target Object ◽  
Correlation Filter ◽  
Correlation Filters ◽  
Object Proposals ◽  
Benchmark Datasets ◽  
Candidate Object

Visual tracking is a challenging task in computer vision due to various appearance changes of the target object. In recent years, correlation filter plays an important role in visual tracking and many state-of-the-art correlation filter based trackers are proposed in the literature. However, these trackers still have certain limitations. Most of existing trackers cannot well deal with scale variation, and they may easily drift to the background in the case of occlusion. To overcome the above problems, we propose a Correlation Filters based Scale Adaptive (CFSA) visual tracker. In the tracker, a modified EdgeBoxes generator, is proposed to generate high-quality candidate object proposals for tracking. The pool of generated candidate object proposals is adopted to estimate the position of the target object using a kernelized correlation filter based tracker with HOG and color naming features. In order to deal with changes in target scale, a scale estimation method is proposed by combining the water flow driven MBD (minimum barrier distance) algorithm with the estimated position. Furthermore, an online updating schema is adopted to reduce the interference of the surrounding background. Experimental results on two large benchmark datasets demonstrate that the CFSA tracker achieves favorable performance compared with the state-of-the-art trackers.

scholarly journals Automatic Segmentation and 3D Printing of A-shaped Manikins using a Bounding Box and Body-feature Points

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jin Young Jung ◽  
Seonkoo Chee ◽  
In Hwan Sul
Keyword(s):  
3D Printing ◽  
Human Body ◽  
Large Scale ◽  
Automatic Segmentation ◽  
Target Object ◽  
Computational Stability ◽  
Segmentation Strategy ◽  
Bounding Box ◽  
3D Printers ◽  
Scan Data

AbstractA novel algorithm for 3D-printing technology was proposed to generate large-scale objects, especially A-shaped manikins or 3D human body scan data. Most of the conventional 3D printers have a finite printing volume, and it is the users’ work to convert the target object into a printable size. In this study, an automatic three-step segmentation strategy was applied to the raw manikin mesh data until the final pieces had a smaller size than the 3D printer’s maximum printing volume, which is generally called “beam length”. Human body feature point information was adopted for fashion and textile researchers to easily specify the desired cutting positions. A simple bounding box, especially orienting bounding box, and modified Boolean operator were proposed to extract the specified segments with computational stability. The proposed method was applied to graphically synthesized manikin data, and 1/8, 1/4, and 1/2 scale manikins were successfully printed, minimizing the amount of support structure.

scholarly journals Pseudo-photometric distances of 30 open clusters

2018 ◽  
Vol 619 ◽  
pp. A132 ◽  
Author(s):  
A. Chelli ◽  
G. Duvert
Keyword(s):  
Spectral Type ◽  
Large Scale ◽  
Open Cluster ◽  
Estimation Method ◽  
Distance Estimation ◽  
Open Clusters ◽  
Distance Modulus ◽  
Stellar Clusters ◽  
The Mean ◽  
The Difference

Aims. We demonstrate that reliable photometric distances of stellar clusters, and more generally of stars, can be obtained using pseudomagnitudes and rough spectral type without having to correct for visual absorption. Methods. We determine the mean absolute pseudomagnitude of all spectral (sub)types between B and K. Distances are computed from the difference between the star’s observed pseudomagnitude and its spectral type’s absolute pseudomagnitude. We compare the distances of 30 open clusters thus derived against the distances derived from TGAS parallaxes. Results. Our computed distances, up to distance modulus 12, agree within 0.1 mag rms with those obtained from TGAS parallaxes, proving excellent distance estimates. We show additionally that there are actually two markedly different distances in the cluster NGC 2264. Conclusions. We suggest that the pseudomagnitude distance estimation method, which is easy to perform, can be routinely used in all large-scale surveys where statistical distances on a set of stars, such as an open cluster, are required.

scholarly journals Distractor-Aware Deep Regression for Visual Tracking

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 387 ◽  
Author(s):  
Ming Du ◽  
Yan Ding ◽  
Xiuyun Meng ◽  
Hua-Liang Wei ◽  
Yifan Zhao
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Test Data ◽  
Loss Function ◽  
State Of The Art ◽  
Target Object ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Training Samples ◽  
Better Than

In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches.

scholarly journals Searching for the shadows of giants: characterizing protoclusters with line of sight Lyman-α absorption

2019 ◽  
Vol 489 (4) ◽  
pp. 5381-5397 ◽  
Author(s):  
Joel S A Miller ◽  
James S Bolton ◽  
Nina Hatch
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
High Redshift ◽  
Line Of Sight ◽  
Small Subset ◽  
Weak Correlation ◽  
Wide Range ◽  
The Mean ◽  
Pass Through ◽  
Damped Systems

ABSTRACT We use state-of-the-art hydrodyamical simulations from the Sherwood, EAGLE, and Illustris projects to examine the signature of Mz = 0 ≃ 1014 M⊙ protoclusters observed in Ly α absorption at z ≃ 2.4. We find that there is a weak correlation between the mass overdensity, δm, and the Ly α effective optical depth relative to the mean, $\delta _{\tau _\textrm{eff}}$, averaged over $15~h^{-1}\, \textrm{cMpc}$ scales, although scatter in the δm–$\delta _{\tau _\textrm{eff}}$ plane means it is not possible to uniquely identify large-scale overdensities with strong Ly α absorption. Although all protoclusters are associated with large-scale mass overdensities, most sightlines through protoclusters in a ∼106$\rm cMpc^{3}$ volume probe the low column density Ly α forest. A small subset of sightlines that pass through protoclusters exhibit coherent, strong Ly α absorption on $15h^{-1}\rm \, cMpc$ scales, although these correspond to a wide range in mass overdensity. Assuming perfect removal of contamination by Ly α absorbers with damping wings, more than half of the remaining sightlines with $\delta _{\tau _{\rm eff}}\gt 3.5$ trace protoclusters. It is furthermore possible to identify a model-dependent $\delta _{\tau _{\rm eff}}$ threshold that selects only protoclusters. However, such regions are rare: excluding absorption caused by damped systems, less than 0.1 per cent of sightlines that pass through a protocluster have $\delta _{\tau _{\rm eff}}\gt 3.5$, meaning that any protocluster sample selected in this manner will also be highly incomplete. On the other hand, coherent regions of Ly α absorption also provide a promising route for identifying and studying filamentary environments at high redshift.

scholarly journals Binary Coding based Label Distribution Learning

2018 ◽  
Author(s):  
Ke Wang ◽  
Xin Geng
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Binary Codes ◽  
Binary Coding ◽  
Benchmark Datasets ◽  
Hamming Space ◽  
The Mean ◽  
Label Distribution Learning ◽  
Distribution Generation ◽  
Label Distribution

Label Distribution Learning (LDL) is a novel learning paradigm in machine learning, which assumes that an instance is labeled by a distribution over all labels, rather than labeled by a logic label or some logic labels. Thus, LDL can model the description degree of all possible labels to an instance. Although many LDL methods have been put forward to deal with different application tasks, most existing methods suffer from the scalability issue. In this paper, a scalable LDL framework named Binary Coding based Label Distribution Learning (BC-LDL) is proposed for large-scale LDL. The proposed framework includes two parts, i.e., binary coding and label distribution generation. In the binary coding part, the learning objective is to generate the optimal binary codes for the instances. We integrate the label distribution information of the instances into a binary coding procedure, leading to high-quality binary codes. In the label distribution generation part, given an instance, the k nearest training instances in the Hamming space are searched and the mean of the label distributions of all the neighboring instances is calculated as the predicted label distribution. Experiments on five benchmark datasets validate the superiority of BC-LDL over several state-of-the-art LDL methods.  

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals Quality Control in 3D Printing: Accuracy Analysis of 3D-Printed Models of Patient-Specific Anatomy

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1021
Author(s):  
Bernhard Dorweiler ◽  
Pia Elisabeth Baqué ◽  
Rayan Chaban ◽  
Ahmed Ghazy ◽  
Oroa Salem
Keyword(s):  
Wall Thickness ◽  
Large Scale ◽  
Fused Deposition Modeling ◽  
Vascular Anatomy ◽  
3D Models ◽  
Patient Specific ◽  
Stl File ◽  
Fused Deposition ◽  
3D Printed ◽  
The Mean

As comparative data on the precision of 3D-printed anatomical models are sparse, the aim of this study was to evaluate the accuracy of 3D-printed models of vascular anatomy generated by two commonly used printing technologies. Thirty-five 3D models of large (aortic, wall thickness of 2 mm, n = 30) and small (coronary, wall thickness of 1.25 mm, n = 5) vessels printed with fused deposition modeling (FDM) (rigid, n = 20) and PolyJet (flexible, n = 15) technology were subjected to high-resolution CT scans. From the resulting DICOM (Digital Imaging and Communications in Medicine) dataset, an STL file was generated and wall thickness as well as surface congruency were compared with the original STL file using dedicated 3D engineering software. The mean wall thickness for the large-scale aortic models was 2.11 µm (+5%), and 1.26 µm (+0.8%) for the coronary models, resulting in an overall mean wall thickness of +5% for all 35 3D models when compared to the original STL file. The mean surface deviation was found to be +120 µm for all models, with +100 µm for the aortic and +180 µm for the coronary 3D models, respectively. Both printing technologies were found to conform with the currently set standards of accuracy (<1 mm), demonstrating that accurate 3D models of large and small vessel anatomy can be generated by both FDM and PolyJet printing technology using rigid and flexible polymers.

scholarly journals Relationship between large-scale ionospheric field-aligned currents and electron/ion precipitations: DMSP observations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Chao Xiong ◽  
Claudia Stolle ◽  
Patrick Alken ◽  
Jan Rauberg
Keyword(s):  
Time Distribution ◽  
Large Scale ◽  
Particle Flux ◽  
Particle Energy ◽  
Lower Latitude ◽  
Particle Precipitation ◽  
Data Set ◽  
The Mean ◽  
Two Parameters ◽  
Meteorological Satellite

Abstract In this study, we have derived field-aligned currents (FACs) from magnetometers onboard the Defense Meteorological Satellite Project (DMSP) satellites. The magnetic latitude versus local time distribution of FACs from DMSP shows comparable dependences with previous findings on the intensity and orientation of interplanetary magnetic field (IMF) By and Bz components, which confirms the reliability of DMSP FAC data set. With simultaneous measurements of precipitating particles from DMSP, we further investigate the relation between large-scale FACs and precipitating particles. Our result shows that precipitation electron and ion fluxes both increase in magnitude and extend to lower latitude for enhanced southward IMF Bz, which is similar to the behavior of FACs. Under weak northward and southward Bz conditions, the locations of the R2 current maxima, at both dusk and dawn sides and in both hemispheres, are found to be close to the maxima of the particle energy fluxes; while for the same IMF conditions, R1 currents are displaced further to the respective particle flux peaks. Largest displacement (about 3.5°) is found between the downward R1 current and ion flux peak at the dawn side. Our results suggest that there exists systematic differences in locations of electron/ion precipitation and large-scale upward/downward FACs. As outlined by the statistical mean of these two parameters, the FAC peaks enclose the particle energy flux peaks in an auroral band at both dusk and dawn sides. Our comparisons also found that particle precipitation at dawn and dusk and in both hemispheres maximizes near the mean R2 current peaks. The particle precipitation flux maxima closer to the R1 current peaks are lower in magnitude. This is opposite to the known feature that R1 currents are on average stronger than R2 currents.

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