Dynamic Background Research Articles

Rapid chromosome evolution and acquisition of thermosensitive stochastic sex determination in nematode androdioecious hermaphrodites

The factors contributing to evolution of androdioecy, the coexistence of hermaphrodites and males such as in Caenorhabditis elegans, remains poorly known. However, nematodes exhibit androdioecy in at last 13 genera with the predatory genus Pristionchus having seven independent transitions towards androdioecy. Nonetheless, associated genomic architecture and sex determination mechanisms are largely known from Caenorhabditis. Here, studying 47 Pristionchus species, we observed repeated chromosome evolution which abolished the ancestral XX/XO sex chromosome system. Two phylogenetically unrelated androdioecious Pristionchus species have no genomic differences between sexes and mating hermaphrodites with males resulted in hermaphroditic offspring only. We demonstrate that stochastic sex determination is influenced by temperature in P. mayeri and P. entomophagus, and CRISPR engineering indicated a conserved role of the transcription factor TRA-1 in P. mayeri. Chromosome-level genome assemblies and subsequent genomic analysis of related Pristionchus species revealed stochastic sex determination to be derived from XY sex chromosome systems through sex chromosome-autosome fusions. Thus, rapid karyotype evolution, sex chromosome evolution and evolvable sex determination mechanisms are general features of this genus, and represent a dynamic background against which androdioecy has evolved recurrently. Future studies might indicate that stochastic sex determination is more common than currently appreciated.

Are background breast parenchymal features on preoperative breast MRI associated with disease-free survival in patients with invasive breast cancer?

To evaluate whether breast parenchymal features of the contralateral breast on preoperative MRI are associated with primary breast cancer characteristics and disease-free survival (DFS) in women with invasive breast cancer. Women with newly diagnosed invasive breast cancer who underwent preoperative breast MRI followed by surgery were retrospectively evaluated. Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI and background diffusion signal (BDS) on diffusion-weighted MRI of the contralateral breast were qualitatively assessed using a four-category scale: minimal, mild, moderate, or marked. Primary breast cancer characteristics were compared based on the degree of BPE or BDS. Cox proportional hazards models were used to evaluate the association between MRI parenchymal features and DFS after adjusting for clinicopathologic features. A total of 515 women (mean age, 54years) were included. Of whom, 46 (8.9%) patients who developed disease recurrence at a median follow-up of 60months were observed. A high level (moderate/marked) of BPE or BDS was associated with younger age (≤ 45) and premenopausal status (all P < 0.05) compared to a low level (minimal/mild), but it was not associated with primary cancer characteristics such as tumor stage, grade, or subtype. Multivariable Cox proportional hazards analysis demonstrated that larger tumor size (> 2cm) (hazard ratio [HR], 3.877; P < . 001), triple-negative subtype (HR, 2.440; P = .013), and axillary node metastasis (HR, 1.823; P = .049) were associated with worse DFS. No associations were observed between background parenchymal features and disease outcomes. MRI parenchymal features, including BPE and BDS, of the contralateral breast showed no associations with primary breast cancer characteristics or DFS in women with invasive breast cancer.

Hard jet substructure in a multistage approach

We present predictions and postdictions for a wide variety of hard jet-substructure observables using a multistage model within the framework. The details of the multistage model and the various parameter choices are described in []. A novel feature of this model is the presence of two stages of jet modification: a high-virtuality phase [modeled using the modular all twist transverse-scattering elastic-drag and radiation model ()], where modified coherence effects diminish medium-induced radiation, and a lower virtuality phase [modeled using the linear Boltzmann transport model ()], where parton splits are fully resolved by the medium as they endure multiple scattering induced energy loss. Energy-loss calculations are carried out on event-by-event viscous fluid dynamic backgrounds constrained by experimental data. The uniform and consistent descriptions of multiple experimental observables demonstrate the essential role of modified coherence effects and the multistage modeling of jet evolution. Using the best choice of parameters from [], and with no further tuning, we present calculations for the medium modified jet fragmentation function, the groomed jet momentum fraction zg and angular separation rg distributions, as well as the nuclear modification factor of groomed jets. These calculations provide accurate descriptions of published data from experiments at the Large Hadron Collider. Furthermore, we provide predictions from the multistage model for future measurements at the BNL Relativistic Heavy Ion Collider. Published by the American Physical Society 2024

Online Tree-Structure-Constrained RPCA for Background Subtraction of X-ray Coronary Angiography Images

Background and objectiveBackground subtraction of X-ray coronary angiograms (XCA) can significantly improve the diagnosis and treatment of coronary vessel diseases. The XCA background is complex and dynamic due to structures with different intensities and independent motion patterns, making XCA background subtraction challenging. MethodsThe current work proposes an online tree-structure-constrained robust PCA (OTS-RPCA) method to subtract the XCA background. A morphological closing operation is used as a pre-processing step to remove large-scale structures like the spine, chest and diaphragm. In the following, the XCA sequence is decomposed into three different subspaces: low-rank background, residual dynamic background and vascular foreground. A tree-structured norm is introduced and applied to the vascular submatrix to guarantee the vessel spatial coherency. Moreover, the residual dynamic background is separately extracted to remove noise and motion artifacts from the vascular foreground. The proposed algorithm also employs an adaptive regularization coefficient that tracks the vessel area changes in the XCA frames. ResultsThe proposed method is evaluated on two datasets of real clinical and synthetic low-contrast XCA sequences of 38 patients using the global and local contrast-to-noise ratio (CNR) and structural similarity index (SSIM) criteria. For the real XCA dataset, the average values of global CNR, local CNR and SSIM are 6.27, 3.07 and 0.97, while these values over the synthetic low-contrast dataset are obtained as 5.15, 2.69 and 0.94, respectively. The implemented quantitative and qualitative experiments verify the superiority of the proposed method over seven selected state-of-the-art methods in increasing the coronary vessel contrast and preserving the vessel structure. ConclusionsThe proposed OTS-RPCA background subtraction method accurately subtracts backgrounds from XCA images. Our method might provide the basis for reducing the contrast agent dose and the number of needed injections in coronary interventions.

Motion-guided small MAV detection in complex and non-planar scenes

In recent years, there has been a growing interest in the visual detection of micro aerial vehicles (MAVs) due to its importance in numerous applications. However, the existing methods based on either appearance or motion features encounter difficulties when the background is complex or the MAV is too small. In this paper, we propose a novel motion-guided MAV detector that can accurately identify small MAVs in complex and non-planar scenes. This detector first exploits a motion feature enhancement module to capture the motion features of small MAVs. Then it uses multi-object tracking and trajectory filtering to eliminate false positives caused by motion parallax. Finally, an appearance-based classifier and an appearance-based detector that operates on the cropped regions are used to achieve precise detection results. Our proposed method can effectively and efficiently detect extremely small MAVs from dynamic and complex backgrounds because it aggregates pixel-level motion features and eliminates false positives based on the motion and appearance features of MAVs. Experiments on the ARD-MAV dataset demonstrate that the proposed method could achieve high performance in small MAV detection under challenging conditions and outperform other state-of-the-art methods across various metrics.

Selective Information Flow for Transformer Tracking

Fully transformer-based one-stream trackers have demonstrated outstanding performance on challenging benchmark datasets over the past three years. These trackers enable bidirectional information flow between all target template and search region tokens to compute attention features without assessing its impact on the tracker’s discriminative capability. Our study found that the information flow from a large portion of background tokens in the search region diminishes the importance of the target-specific features of the template. Moreover, previous transformer-based trackers fail to consider cues from the dynamic background region, even though it contains information about distracting similar objects. To address the identified limitations in transformer tracking, we propose a novel Selective Information Flow Tracking (SIFTrack) framework to enhance the tracker’s discriminative capability by selectively allowing information flow between different types of token groups. In the early encoder layers of the proposed SIFTrack, interactions from all search tokens to target template tokens are blocked to enrich target-specific feature extraction. In the deeper encoder layers, search tokens are partitioned into target and non-target tokens based on their attention scores. Then, bidirectional flow from target search tokens to template tokens is allowed to capture the appearance changes of the target. In addition, by including tokens from the dynamic background, SIFTrack effectively avoids distractor objects by capturing cues from the area surrounding the target. The proposed SIFTrack demonstrated outstanding performance in challenging benchmarks, particularly excelling in the one-shot tracking benchmark GOT-10k, achieving an average overlap of 74.6%. The code, models, and results of this work are available at https://github.com/JananiKugaa/SIFTrack.

Multi-spectral image fusion for moving object detection

This study delves into the problem of moving object detection in infrared and visible images. While existing approaches primarily focus on single-task detection using single-spectral image data, such as thermal infrared or visible images, they often ignore the differences between different spectral images and the interconnectedness of related tasks such as image fusion and segmentation. To tackle these problems, we present a novel multi-spectral image fusion network with quality and semantic awareness for moving object detection (MOD), particularly in scenarios where ground truth labels for both infrared and visible images are unavailable. Our approach fuses multi-spectral images, incorporating additional subtasks in the image fusion to obtain content and quality perception of infrared and visible images. In addition, we design a novel residual global perception module (RGPM) and multi-spectral fusion loss, which can capture more hidden features and contextual information across various scales. This enhanced capability leads to more precise detection and tracking of moving objects, particularly in challenging situations involving occlusions and dynamic backgrounds. Compared with single-spectral moving object detection optimization, the hurdles of utilizing deep learning for multi-spectral image fusion, e.g., without ground truth labels and harmful noise, are significantly mitigated. Extensive quantitative and qualitative comparative experiments demonstrate its effectiveness, robustness, and superior performance compared to contemporary methods. Concisely, the proposed fusion representation learning has gained 44.2%,31.2%,36.3%,41.6%,5.3%,31.2%,76.2% on EI, SF, DF, AG, MI, SD and Nabf metrics compared with the best competitors.

Mutual correlation and chaotic behavior in phantom AdS black holes in both dynamic and static backgrounds

We research the mutual correlation and chaotic behavior in phantom Anti-de Sitter (AdS) black holes based mainly on shock wave geometry. In the static background of the phantom spacetime, by analyzing mutual correlation, we find out that the greater the phantom dark energy parameter is, the smaller the mutual correlation becomes, which implies phantom dark energy can destroy bilateral correlation of the two sub-regions from the AdS boundary. In the dynamic background, the results show that, the greater the applied energy perturbation is, the smaller the mutual correlation becomes. Especially, the bigger the phantom dark energy is exerted, the faster the mutual correlation destroys. This is similar to the butterfly effect of chaos theory.

Frame-Based Change Detection Using Histogram and Threshold to Separate Moving Objects from Dynamic Background

Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.

Lester: Rotoscope Animation through Video Object Segmentation and Tracking

This article introduces Lester, a novel method to automatically synthesize retro-style 2D animations from videos. The method approaches the challenge mainly as an object segmentation and tracking problem. Video frames are processed with the Segment Anything Model (SAM) and the resulting masks are tracked through subsequent frames with DeAOT, a method of hierarchical propagation for semi-supervised video object segmentation. The geometry of the masks’ contours is simplified with the Douglas–Peucker algorithm. Finally, facial traits, pixelation and a basic rim light effect can be optionally added. The results show that the method exhibits an excellent temporal consistency and can correctly process videos with different poses and appearances, dynamic shots, partial shots and diverse backgrounds. The proposed method provides a more simple and deterministic approach than diffusion models based video-to-video translation pipelines, which suffer from temporal consistency problems and do not cope well with pixelated and schematic outputs. The method is also more feasible than techniques based on 3D human pose estimation, which require custom handcrafted 3D models and are very limited with respect to the type of scenes they can process.

Looming Detection in Complex Dynamic Visual Scenes by Interneuronal Coordination of Motion and Feature Pathways

Detecting looming signals for collision avoidance encounters challenges in real‐world scenarios, where moving backgrounds can interfere as an agent navigates through complex natural environments. Remarkably, even insects with limited neural systems adeptly respond to looming stimuli while in motion at high speeds. Existing insect‐inspired looming detection models typically rely on either motion‐pathway or feature‐pathway signals, yet both are susceptible to dynamic visual scene interference. Coordinating interneuron signals from both pathways can enhance the looming detection performance under dynamic conditions. An artificial neural network is employed to construct a combined‐pathway model based on Drosophila anatomy. The model outperforms state‐of‐the‐art bio‐inspired looming‐detection models in tasks involving dynamic backgrounds, simulated by animated 2D‐moving natural scenes or recorded in reality when an unmanned aerial vehicle performs obstacle collision avoidance tasks. Notably, by combining neural anatomy architecture and appropriate multiobjective tasks, the model exhibits convergent neural dynamics with biological counterparts post‐training, offering network explanations and mechanistic insights. Specifically, a multiplicative interneuron operation enhances looming signal patterns and reduces background interferences, generalizing to more complex scenarios, such as AirSim 3D environments and real‐world situations. The work introduces testable biological hypotheses and a promising bioinspired solution for looming detection in dynamic visual environments.

Identification Method for Spring Dust Intensity Levels Based on Multiple Remote Sensing Parameters

The advancement of more precise remote sensing inversion technology for dust aerosols has long been a hot topic in the field of the atmospheric environment. In 2023, China experienced 18 dust-related weather events, predominantly in spring. These high-intensity and frequent dust events have attracted considerable attention. However, gridded observation data of dust intensity levels are not collected in current dust monitoring and forecasting operations. Based on the Himawari 9 geostationary satellite data, this study establishes a new method to identify spring dust events. This method integrates the brightness temperature difference method and the multiple infrared dust index, taking into account the response discrepancies of the multiple infrared dust index under various underlying surfaces. Furthermore, by obtaining dynamic background brightness temperature values eight times a day, threshold statistics are applied to analyze the correlation between the infrared difference dust index and ground-observed dust level, so as to establish a satellite-based near-surface dust intensity level identification algorithm. This algorithm aims to improve dust detection accuracy, and to provide more effective gridded observation support for dust forecasting and monitoring operations. The test results indicate that the algorithm can effectively identify the presence or absence of dust, with a misjudgment rate of less than 3%. With regard to dust intensity, the identification of blowing sand and floating dust aligns relatively well with ground-based observations, but notable uncertainties exist in determining a dust intensity of sand-storm level or above. Among these uncertainties, the differences between ground-based observations and satellite identification caused by non-grounded dust in the upper air, and the selection of dust identification thresholds, are two important error sources in the dust identification results of this study.

Disordered magnetic ground state in a quasi-1-D d4columnar iridate Sr3LiIrO6.

Spin-orbit coupling (SOC) offers a large variety of novel and extraordinary magnetic and electronic properties in otherwise 'ordinary pool' of heavy ion oxides. Here we present a detailed study on an apparently isolated hexagonal 2Hspin-chain d4iridate Sr3LiIrO6(SLIO) with geometric frustration. Our structural studies reveal Li-Ir chemical order with desired stoichiometry in this compound, while x-ray absorption together with x-ray photoemission spectroscopic characterizations establish pure 5+ valence of Ir. We have established a magnetic ground state with finite Ir5+magnetic moments in this compound, contrary to the anticipated nonmagnetic Jeff= 0 state, through combined dc susceptibility,7Li nuclear magnetic resonance (NMR), muon spin relaxation (μSR) and ab-initio electronic structure studies. These investigations together with ac magnetic susceptibility and specific heat measurements reveal that despite having noticeable antiferromagnetic correlation among the Ir5+local moments, this system does not magnetically order down to at least 0.05 K, possibly due to geometrical exchange frustration, arising from the comparable nearest- and next-nearest-neighbor interchain Ir-O-O-Ir superexchange interaction strengths with opposite signs. However, the zero-field (ZF) μSR analysis shows emergence of a considerable proportion of spin-freezing on top of a spin-fluctuating dynamic magnetic background down to the lowest measured&#xD;temperature of 1.7 K, possibly due to some inhomogeneity and/or the much stronger intra-column Ir-Ir magnetic exchange interaction&#xD;strength relative to the inter-column Ir-Ir ones. The linear temperature dependence of the magnetic specific heat (Cm) in both zero and applied magnetic fields, plus the power-law behavior of the NMR spin-lattice relaxation rate suggest a gapless spinon density of states in this charge gapped disordered magnetic ground state of Sr3LiIrO6.

Moving object detection via feature extraction and classification

Abstract Foreground segmentation (FS) plays a fundamental and important role in computer vision, but it remains a challenging task in dynamic backgrounds. The supervised method has achieved good results, but the generalization ability needs to be improved. To address this challenge and improve the performance of FS in dynamic scenarios, a simple yet effective method has been proposed that leverages superpixel features and a one-dimensional convolution neural network (1D-CNN) named SPF-CNN. SPF-CNN involves several steps. First, the coined Iterated Robust CUR (IRCUR) is utilized to obtain candidate foregrounds for an image sequence. Simultaneously, the image sequence is segmented using simple linear iterative clustering. Next, the proposed feature extraction approach is applied to the candidate matrix region corresponding to the superpixel block. Finally, the 1D-CNN is trained using the obtained superpixel features. Experimental results demonstrate the effectiveness of SPF-CNN, which also exhibits strong generalization capabilities. The average F1-score reaches 0.83.

A Review of the Genetic Mechanism of Megacrystalline Uraninite in the Kangdian Region, China

Naturally occurring granular uranium particles are typically fine and are often found as accessory minerals. However, reports of megacrystalline uraninite are rare. The discovery of megacrystalline uraninite is a significant achievement in uranium prospecting and mineralogy in the Kangdian region and China. Our team’s research and review of previous studies have led to a systematic summary of the formation age, genetic types, relationship with migmatization, and metallogenic dynamic background of megacrystalline uraninite in the Kangdian region. The key findings are as follows: (1) the formation age of megacrystalline uraninite is Neoproterozoic (790–770 Ma); (2) migmatization preceded uranium mineralization; (3) the formation of megacrystalline uraninite is linked to high-temperature, low-pressure metamorphism caused by partial melting; (4) and the formation of megacrystalline uraninite may be associated with the Rodinia rifting event. This review aims to enhance our understanding of uranium mineralization during the Neoproterozoic in China and worldwide.

Metabolite Profiling Analysis of the Tongmai Sini Decoction in Rats after Oral Administration through UHPLC-Q-Exactive-MS/MS.

Tongmai Sini decoction (TSD), the classical prescriptions of traditional Chinese medicine, consisting of three commonly used herbal medicines, has been widely applied for the treatment of myocardial infarction and heart failure. However, the absorbed components and their metabolism in vivo of TSD still remain unknown. In this study, a reliable and effective method using ultra-performance liquid chromatography coupled with hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Exactive-MS/MS) was employed to identify prototype components and metabolites in vivo (rat plasma and urine). Combined with mass defect filtering (MDF), dynamic background subtraction (DBS), and neutral loss filtering (NLF) data-mining tools, a total of thirty-two major compounds were selected and investigated for their metabolism in vivo. As a result, a total of 82 prototype compounds were identified or tentatively characterized in vivo, including 41 alkaloids, 35 phenolic compounds, 6 saponins. Meanwhile, A total of 65 metabolites (40 alkaloids and 25 phenolic compounds) were tentatively identified. The metabolic reactions were mainly hydrogenation, demethylation, hydroxylation, hydration, methylation, deoxylation, and sulfation. These findings will be beneficial for an in-depth understanding of the pharmacological mechanism and pharmacodynamic substance basis of TSD.

Numerical simulation of fault activity in the Qilian tectonic belt and dynamic background of Menyuan earthquake series

Some faults in the Qilian tectonic belt are seismogenic areas for strong earthquakes, including the western section of the Lenglongling fault, which frequently occures moderate-strong Menyuan earthquakes. In this study, using GPS velocity data of 1991–2015 as the boundary constraints, a three-dimensional viscoelastic finite element dynamic model is constructed by comprehensively considering the regional dynamic environment, crust-mantle transverse-longitudinal inhomogeneity, and spatial spreading of a complex fault system. The objective is to investigate the long-period characteristics of fault movement and stress change in the Qilian tectonic belt caused by tectonic loading, and to discuss the seismogenic conditions of the Menyuan earthquakes. The results show that the annual change of long-period movement and stress of the major faults in the Qilian Mountain tectonic zone are characterized by significant segmentation. Due to its unique geometric bend morphology, the western section of Lenglongling fault has a low movement rate, significant slip deficit and high shear stress accumulation, which are conducive to the gestation and occurrence of earthquakes. Furthermore, the seismogenic area of the Menyuan earthquake series is jointly subjected to NE-SW compressive and NW-SE tensile stress fields, and the maximum shear stress and elastic strain energy accumulate faster than in the surrounding areas. Overall, the western section of the Lenglongling fault has a strong dynamic background and favorable conditions for the frequent occurrence of the Menyuan earthquake series.

Full‐Color Electrochromics with Donor‐Gradient Architectures for Chameleon‐Like Adaptive Camouflage

AbstractMulticolor electrochromics have emerged as a promising solution for achieving active color modulation akin to chameleon‐like adaptive camouflage. However, the development of electrochromics with full‐color tunability still remains challenging, given the weak molecular polarity of inherent active materials and the difficulty in forming polarizer orbitals under varying applied voltages. Employing molecular polarity adjustments, a novel electrochromic polymer is successfully developed with a donor‐gradient design of molecular architecture, enabling comprehensive full‐color tunability. The polymer demonstrates outstanding overall electrochromic performance, including fast switching (around 1.4 s), high coloration efficiency (395.1 cm2 C−1@550 nm) and excellent reversibility (over 92% retention after 5000 cycles). Leveraging the full‐color tunability and rapid response, the electrochromic polymer is further integrated with sensing and control modules to create an environmental adaptive camouflage prototype system capable of seamlessly blending colors with dynamic background environments. This work is expected to provide a straightforward approach for developing full‐color electrochromics for electrochromic camouflage applications.

Cenozoic structure of the Zhu 3 Depression in the Pearl River Mouth Basin and its response to the dynamic background of the South China Sea

The Zhu 3 Depression is located at the transition between the South China Sea and the South China Block. The Cenozoic structure reflects the dynamic background of the South China Sea. We used 3D seismic and log data to study the structural evolution of the Zhu 3 Depression. We defined the characteristics of Cenozoic rift deformation and the migration of the depocentre based on fault activity and the distribution of sediment thickness. The spatial and temporal differences in the Cenozoic structure of the Zhu 3 Depression are due to the influence of pre-existing faults and regional stresses. Our results show that there are three populations of faults in the Zhu 3 Depression, striking NE, east–west and NW. The NE-striking faults are mainly large-scale boundary faults. The east–west-striking faults are small and were activated in the later stages of deformation. The NW-striking faults are continuous at depth, but are en echelon and parallel to each other at shallow depths. Based on these results, we suggest that the NE- and NW-striking pre-existing faults divide the Zhu 3 Depression into different structural zones and controlled the differences in deformation in the basin. The regional stress direction, which is controlled by regional plate interactions and the Red River Fault Zone, changed clockwise from NW-trending during the Paleocene to NE-trending during the Mid-Miocene. These findings will contribute to a better understanding of the evolution of the entire South China Sea. Thematic collection: This article is part of the Emerging knowledge on the tectonics of the South China Sea collection available at: https://www.lyellcollection.org/topic/collections/south-china-sea

Anti-UAV410: A Thermal Infrared Benchmark and Customized Scheme for Tracking Drones in the Wild.

The perception of drones, also known as Unmanned Aerial Vehicles (UAVs), particularly in infrared videos, is crucial for effective anti-UAV tasks. However, existing datasets for UAV tracking have limitations in terms of target size and attribute distribution characteristics, which do not fully represent complex realistic scenes. To address this issue, we introduce a generalized infrared UAV tracking benchmark called Anti-UAV410. The benchmark comprises a total of 410 videos with over 438 K manually annotated bounding boxes. To tackle the challenges of UAV tracking in complex environments, we propose a novel method called Siamese drone tracker (SiamDT). SiamDT incorporates a dual-semantic feature extraction mechanism that explicitly models targets in dynamic background clutter, enabling effective tracking of small UAVs. The SiamDT method consists of three key steps: Dual-Semantic RPN Proposals (DS-RPN), Versatile R-CNN (VR-CNN), and Background Distractors Suppression. These steps are responsible for generating candidate proposals, refining prediction scores based on dual-semantic features, and enhancing the discriminative capacity of the trackers against dynamic background clutter, respectively. Extensive experiments conducted on the Anti-UAV410 dataset and three other large-scale benchmarks demonstrate the superior performance of the proposed SiamDT method compared to recent state-of-the-art trackers.