Template Image Research Articles

Visual tracking algorithm based on template updating and dual feature enhancement

Aiming at the problem of tracking failure due to target deformation, flipping and occlusion in visual tracking, a template updating algorithm based on image structural similarity is proposed by dynamically updating the template to adapt to the changes of the target during tracking, specifically, a queue is used to save the recent N -frame tracking results, and a decision is made on whether to update the template or not based on the structural similarity score between the current tracking results and the template image, and if updated, the template is matched from the historical N -frame tracking results as the new template. If updated, the optimal target image is matched from the historical N -frame tracking results as a new template for subsequent tracking. The tracking feature enhancement module and segmentation feature enhancement module are also designed based on SiamMask network. The tracking feature enhancement module consists of non-local operations and convolutional downsampling, which is used to establish contextual correlation, enhance the target features, suppress the background interference, improve the tracking robustness, and solve the feature attenuation problem due to the occlusion of the target. The segmentation feature enhancement module introduces the convolutional block attention module and deformable convolution to improve the network's ability to capture channel and spatial features, adaptively learn the shape and contour information of the target, and enhance the network's segmentation accuracy of the tracked target. , which in turn improves the tracking accuracy. Experiments show that the proposed algorithm performs well and stably in solving the above problems, improving the expected average overlap rate by 5.2%, 5.3%, and 2.5%, and the robustness by 6%, 7.9%, and 15.6%on the VOT2016, VOT2018, and VOT2019 datasets , respectively, and achieving a real-time speed of 91 frames per second, when compared to the baseline SiamMask.

A Fast Face Changing Method Adaptable to Various Postures

Image face-changing technology is influenced by skin color and posture. After changing faces, the contour information or skin color information of the user's face will be lost, resulting in low similarity between the changed image and the user's face in the source image. This paper introduces a rapid face-changing method that can adapt to various poses. Regardless of whether the facial poses in the source image and template image differ significantly, this method can retain the facial contours and skin tone in the user's source image without deformation, resulting in a high similarity between the transformed image and the face in the source image. Additionally, it offers low computational complexity, quick processing speed, and a small amount of computational effort.

Image Registration Algorithm for Stamping Process Monitoring Based on Improved Unsupervised Homography Estimation

Homography estimation is a crucial task in aligning template images with target images in stamping monitoring systems. To enhance the robustness and accuracy of homography estimation against random vibrations and lighting variations in stamping environments, this paper proposes an improved unsupervised homography estimation model. The model takes as input the channel-stacked template and target images and outputs the estimated homography matrix. First, a specialized deformable convolution module and Group Normalization (GN) layer are introduced to expand the receptive field and enhance the model’s ability to learn rotational invariance when processing large, high-resolution images. Next, a multi-scale, multi-stage unsupervised homography estimation network structure is constructed to improve the accuracy of homography estimation by refining the estimation through multiple stages, thereby enhancing the model’s resistance to scale variations. Finally, stamping monitoring image data is incorporated into the training through data fusion, with data augmentation techniques applied to randomly introduce various levels of perturbation, brightness, contrast, and filtering to improve the model’s robustness to complex changes in the stamping environment, making it more suitable for monitoring applications in this specific industrial context. Compared to traditional methods, this approach provides better homography matrix estimation when handling images with low texture, significant lighting variations, or large viewpoint changes. Compared to other deep-learning-based homography estimation methods, it reduces estimation errors and performs better on stamping monitoring images, while also offering broader applicability.

Watershed regions are more susceptible to tissue microstructural injury in multiple sclerosis.

Histopathologic studies report higher concentrations of multiple sclerosis white matter lesions in watershed areas of the brain, suggesting that areas with relatively lower oxygen levels may be more vulnerable to disease. However, it is unknown at what point in the disease course lesion predilection for watershed territories begins. Accordingly, we studied a cohort of people with newly diagnosed disease and asked whether (1) white matter lesions disproportionally localize to watershed-regions and (2) the degree of microstructural injury in watershed-lesions is more severe. Fifty-four participants, i.e. 38 newly diagnosed people with multiple sclerosis, clinically isolated syndrome or radiologically isolated syndrome, and 16 age- and sex-matched healthy controls underwent brain magnetic resonance imaging. T1-weighted and T2-weighted fluid-attenuated inversion recovery sequences, selective inversion recovery quantitative magnetisation transfer images, and the multi-compartment diffusion imaging with the spherical mean technique were acquired. We computed the macromolecular-to-free pool size ratio, and the apparent axonal volume fraction maps to indirectly estimate myelin and axonal integrity, respectively. We produced a flow territory atlas in each subject's native T2-weighted fluid-attenuated inversion recovery images using a T1-weighted magnetic resonance imaging template in the Montreal Neurological Institute 152 space. Lesion location relative to the watershed, non-watershed and mixed brain vascular territories was annotated. The same process was performed on the T2-weighted fluid-attenuated inversion recovery images of the healthy controls using 294 regions of interest. Generalized linear mixed models for continuous outcomes were used to assess differences in size, pool size ratio and axonal volume fraction between lesions/regions of interests (in healthy controls) situated in different vascular territories. In patients, we assessed 758 T2-lesions and 356 chronic black holes (cBHs). The watershed-territories had higher relative and absolute concentrations of T2-lesions (P≤0.041) and cBHs (P≤0.036) compared to either non-watershed- or mixed-zones. T2-lesions in watershed-areas also had lower pool size ratio relative to T2-lesions in either non-watershed- or mixed-zones (P = 0.039). These results retained significance in the sub-cohort of people without vascular comorbidities and when accounting for periventricular lesions. In healthy controls, axonal volume fraction was higher only in mixed-areas regions of interest compared to non-watershed-ones (P = 0.008). No differences in pool size ratio were seen. We provide in vivo evidence that there is an association between arterial vascularisation of the brain and multiple sclerosis-induced tissue injury as early as the time of disease diagnosis. Our findings underline the importance of oxygen delivery and healthy arterial vascularisation to prevent lesion formation and foster a better outcome in multiple sclerosis.

Neuroinflammation Parallels 18F-PI-2620 Positron Emission Tomography Patterns in Primary 4-Repeat Tauopathies.

Preclinical, postmortem, and positron emission tomography (PET) imaging studies have pointed to neuroinflammation as a key pathophysiological hallmark in primary 4-repeat (4R) tauopathies and its role in accelerating disease progression. We tested whether microglial activation (1) progresses in similar spatial patterns as the primary pathology tau spreads across interconnected brain regions, and (2) whether the degree of microglial activation parallels tau pathology spreading. We examined in vivo associations between tau aggregation and microglial activation in 31 patients with clinically diagnosed 4R tauopathies, using 18F-PI-2620 PET and 18F-GE180 (translocator protein [TSPO]) PET. We determined tau epicenters, defined as subcortical brain regions with highest tau PET signal, and assessed the connectivity of tau epicenters to cortical regions of interest using a 3-T resting-state functional magnetic resonance imaging template derived from age-matched healthy elderly controls. In 4R tauopathy patients, we found that higher regional tau PET covaries with elevated TSPO-PET across brain regions that are functionally connected to each other (β = 0.414, P < 0.001). Microglial activation follows similar distribution patterns as tau and distributes primarily across brain regions strongly connected to patient-specific tau epicenters (β = -0.594, P < 0.001). In these regions, microglial activation spatially parallels tau distribution detectable with 18F-PI-2620 PET. Our findings indicate that the spatial expansion of microglial activation parallels tau distribution across brain regions that are functionally connected to each other, suggesting that tau and inflammation are closely interrelated in patients with 4R tauopathies. The combination of in vivo tau and inflammatory biomarkers could therefore support the development of immunomodulatory strategies for disease-modifying treatments in these conditions. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Diffusion tensor transformation for personalizing target volumes in radiation therapy

Diffusion tensor imaging (DTI) is used in tumor growth models to provide information on the infiltration pathways of tumor cells into the surrounding brain tissue. When a patient-specific DTI is not available, a template image such as a DTI atlas can be transformed to the patient anatomy using image registration. This study investigates a model, the invariance under coordinate transform (ICT), that transforms diffusion tensors from a template image to the patient image, based on the principle that the tumor growth process can be mapped, at any point in time, between the images using the same transformation function that we use to map the anatomy. The ICT model allows the mapping of tumor cell densities and tumor fronts (as iso-levels of tumor cell density) from the template image to the patient image for inclusion in radiotherapy treatment planning. The proposed approach transforms the diffusion tensors to simulate tumor growth in locally deformed anatomy and outputs the tumor cell density distribution over time. The ICT model is validated in a cohort of ten brain tumor patients. Comparative analysis with the tumor cell density in the original template image shows that the ICT model accurately simulates tumor cell densities in the deformed image space. By creating radiotherapy target volumes as tumor fronts, this study provides a framework for more personalized radiotherapy treatment planning, without the use of additional imaging.

EHIR: Energy-based Hierarchical Iterative Image Registration for Accurate PCB Defect Detection

Printed Circuit Board (PCB) Surface defect detection is crucial to ensure the quality of electronic products in manufacturing industry. Detection methods can be divided into non-referential and referential methods. Non-referential methods employ designed rules or learned data distribution without template images but are difficult to address the uncertainty and subjectivity issues of defects. In contrast, referential methods use templates to achieve better performance but rely on precise image registration. However, image registration is especially challenging in feature extracting and matching for PCB images with defective, reduplicated or less features. To address these issues, we propose a novel Energy-based Hierarchical Iterative Image Registration method (EHIR) to formulate image registration as an energy optimization problem based on the edge points rather than finite features. Our framework consists of three stages: Edge-guided Energy Transformation (EET), EHIR and Edge-guided Energy-based Defect Detection (EEDD). The novelty is that the consistency of contours contributes to aligning images and the difference is highlighted for defect location. Extensive experiments show that this method has high accuracy and strong robustness, especially in the presence of defect feature interference, where our method demonstrates an overwhelming advantage over other methods.

Construction and validation of a brain magnetic resonance imaging template for normal older Koreans

BackgroundSpatial normalization to a standardized brain template is a crucial step in magnetic resonance imaging (MRI) studies. Brain templates made from sufficient sample size have low brain variability, improving the accuracy of spatial normalization. Using population-specific template improves accuracy of spatial normalization because brain morphology varies according to ethnicity and age.MethodsWe constructed a brain template of normal Korean elderly (KNE200) using MRI scans 100 male and 100 female aged over 60 years old with normal cognition. We compared the deformation after spatial normalization of the KNE200 template to that of the KNE96, constructed from 96 cognitively normal elderly Koreans and to that of the brain template (OCF), constructed from 434 non-demented older Caucasians to examine the effect of sample size and ethnicity on the accuracy of brain template, respectively. We spatially normalized the MRI scans of elderly Koreans and quantified the amount of deformations associated with spatial normalization using the magnitude of displacement and volumetric changes of voxels.ResultsThe KNE200 yielded significantly less displacement and volumetric change in the parahippocampal gyrus, medial and posterior orbital gyrus, fusiform gyrus, gyrus rectus, cerebellum and vermis than the KNE96. The KNE200 also yielded much less displacement in the cerebellum, vermis, hippocampus, parahippocampal gyrus and thalamus and much less volumetric change in the cerebellum, vermis, hippocampus and parahippocampal gyrus than the OCF.ConclusionKNE200 had the better accuracy than the KNE96 due to the larger sample size and was far accurate than the template constructed from elderly Caucasians in elderly Koreans.

125I seed brachytherapy for non-central pelvic recurrence of cervical cancer after external beam radiotherapy

ObjectiveTo investigate the efficacy of 125I seed brachytherapy for non-central pelvic recurrence of cervical cancer after external beam radiotherapy, and to analyze the clinical influential factors.MethodsBetween June 2015 and April 2022, 32 patients with 41 lesions were treated with 125I seed brachytherapy. The seeds were implanted under the guidance of CT and/or 3D-printed template images at a median dose of 100 Gy (range, 80–120 Gy), and the local control rate (LCR) and survival rates were calculated. We used multivariate logistic regression to identify prognosis predictors, and receiver operating characteristic (ROC) curve analysis to determine the optimal cut-off values.ResultsThe median follow-up was 48.52 months (range, 4–86 months), and the 6-, 12-, and 24-month LCR was 88.0%, 63.2%, and 42.1%, respectively. The 1- and 2-year survival rates were 36% and 33%, respectively, and the median survival time was 13.26 months. No significant adverse events occurred. Multivariate regression analysis showed that tumor diameter, tumor stage, and LCR were independent factors influencing survival. ROC curve analysis showed that the area under the curve for tumor diameter and D90 were 0.765 and 0.542, respectively, with cut-off values of 5.3 cm and 108.5 Gy.ConclusionsThe present findings indicate that 125I seed brachytherapy is feasible for treating non-central pelvic recurrence of cervical cancer after external beam radiotherapy. Further, tumor diameter < 5.3 cm and immediate postoperative D90 > 108.5 Gy were associated with better efficacy.

Study on the Association between anterior tooth Colour Preference based on the FAHP and individual factors.

The relationship between tooth colour and individual satisfaction in oral aesthetics has long been a topic of interest. In this study, we utilized the fuzzy analytic hierarchy process (FAHP) to investigate the impacts of sex and age on tooth colour preference. The findings of this study should provide a scientific basis for oral aesthetic practice. In the current study, a random selection method was employed, and a survey was completed by 120 patients. To obtain tooth colour data, standard tooth colour charts were used. Smile photos were taken as template images using a single-lens reflex camera. The FAHP was utilized to conduct a weight analysis of tooth colour preferences among patients of different sexes and age groups. There were significant differences in tooth colour preference based on sex and age. Men tend to prefer the B1 colour, while women may prioritize the aesthetic effects of other colours. Additionally, as patients age, their preferences for tooth colour become more diverse. These findings offer valuable insights for oral aesthetics practitioners, enabling them to better address the aesthetic needs of patients across different sexes and ages. This knowledge can aid in the development of more personalized treatment plans that align with patients' expectations. In this study, we utilized scientific analysis methods to quantify the popularity of different tooth colours among various groups of people. By doing so, we established a scientific foundation for clinical practice. The findings of this study offer valuable insights for oral aesthetic research, enhancing our understanding of tooth colour. Additionally, these findings have practical applications in the field of oral medicine, potentially improving patients' quality of life and overall oral health.

An Integrated Way to Monitor the Health Condition of Building Facades by Analyzing Thermal Infrared Images

Thermography is widely used for defect detection through analysing thermography. Thermography is used to record the surface temperatures with a thermal infrared camera. Visual interpretation can be employed to identify defects according to the differences in surface temperatures. Recently, image-processing techniques have provided a quantitative way to detect defects rather than visual interpretation. Robust principal component analysis (RPCA) is employed to analyze a series of thermal infrared images [1]. Traditionally, principal component analysis (PCA) is used to investigate a series of thermal infrared images by projecting the thermal data onto a low-dimension space such that a template image inherits the significant characteristics of the given thermal data. However, PCA introduces noise effects such that the template generated with PCA contains the noise effects. RPCA offers a similar way to project the given data onto a low-dimension space and a feature space and provides a robust way to depress the noise effects. With the introduction of RPCA, a template image can be selected from the low- dimension space, and image segmentation is employed to segment the template. Those segmented regions illustrated on the segmented template indicate the surface temperature differences, and those differences can be used as clues to identify the defects.

Identification of Milling Cutter Wear State under Variable Working Conditions Based on Optimized SDP

Traditional data-driven tool wear state recognition methods rely on complete data under targeted working conditions. However, in actual cutting operations, working conditions vary, and data for many conditions lack labels, with data distribution characteristics differing between conditions. To address these issues, this article proposes a method for recognizing the wear state of milling cutters under varying working conditions based on an optimized symmetrized dot pattern (SDP). This method utilizes complete data from source working conditions for representation learning, transferring a generalized milling cutter wear state recognition model to varying working condition scenarios. By leveraging computer image processing features, the vibration signals produced by milling are converted into desymmetrization dot pattern images. Clustering analysis is used to extract template images of different wear states, and differential evolution algorithms are employed to adaptively optimize parameters using the maximization of Euclidean distance as an indicator. Transfer learning with a residual network incorporating an attention mechanism is used to recognize the wear state of milling cutters under varying working conditions. The experimental results indicate that the method proposed in this paper reduces the impact of working condition changes on the mapping relationship of milling cutter wear states. In the wear state identification experiment under varying conditions, the accuracy reached 97.39%, demonstrating good recognition precision and generalization ability.

Which images to label for few-shot medical image analysis?

The success of deep learning methodologies hinges upon the availability of meticulously labeled extensive datasets. However, when dealing with medical images, the annotation process for such abundant training data often necessitates the involvement of experienced radiologists, thereby consuming their limited time resources. In order to alleviate this burden, few-shot learning approaches have been developed, which manage to achieve competitive performance levels with only several labeled images. Nevertheless, a crucial yet previously overlooked problem in few-shot learning is about the selection of template images for annotation before learning, which affects the final performance. In this study, we propose a novel TEmplate Choosing Policy (TECP) that aims to identify and select “the most worthy” images for annotation, particularly within the context of multiple few-shot medical tasks, including landmark detection, anatomy detection, and anatomy segmentation. TECP is composed of four integral components: (1) Self-supervised training, which entails training a pre-existing deep model to extract salient features from radiological images; (2) Alternative proposals for localizing informative regions within the images; and (3) Representative Score Estimation, which involves the evaluation and identification of the most representative samples or templates. (4) Ranking, which rank all candidates and select one with highest representative score. The efficacy of the TECP approach is demonstrated through a series of comprehensive experiments conducted on multiple public datasets. Across all three medical tasks, the utilization of TECP yields noticeable improvements in model performance.

A Robust Registration Method for Multi-view SAR Images based on Best Buddy Similarity

Abstract. Due to the influence of imaging angle and terrain undulation, multi-view synthetic aperture radar (SAR) images are difficult to be directly registered by traditional methods. Although feature matching solves the issue of image rotation and maintains scale invariance, these methods often lead to non-uniformity of interest points and may not achieve subpixel accuracy. The traditional template matching method makes it difficult to generate correct matches for multi-view SAR oblique images. In this paper, a multi-view SAR image template matching method based on Best Buddy Similarity (BBS) is proposed to solve the traditional methods' problem. Firstly, the initial correspondences between images are established according to the Range-Doppler model of SAR images. Secondly, a sliding window search is performed on the established correspondence, the BBS is calculated, and the subpixel locations of the peaks on the similarity map are estimated to achieve a fine match. In the calculation process of BBS, the SAR-ROEWA operator is used to suppress the speckle noise of SAR images. The experiment demonstrated that SAR-BBS can accurately match SAR images with large rotation angle. The peak value on the search window is significant. The registration accuracy of SAR-BBS outperforms the other state-of-the-art methods.

Few-shot SAR vehicle target augmentation based on generative adversarial networks

Abstract. The study of few-shot SAR image generation is an effective way to expand the SAR dataset, which not only provides diversified data support for SAR target classification, but also provides a high-fidelity false image template for SAR deceptive jamming. In this paper, we have constructed a multi-frequency and multi-target type SAR vehicle imagery dataset that encompasses frequencies such as X, Ka, P, and S bands. The vehicle types are coaster, suv and cabin. Subsequently, we utilized various Generative Adversarial Networks for image generation from the SAR vehicle dataset. The experimental result indicates that the images generated by the DCGAN and the LSGAN models are of superior quality. Furthermore, we employed different recognition networks to evaluate the classification accuracy of the generated images. Of all the frequency bands, the Ka band generated images achieved the highest recognition rate, with an accuracy of up to 99%. Under conditions of a limited number of samples, the LSGAN model performed the best, reaching a classification recognition rate of 71.48% with a dataset of only 20 samples. Finally, we use a conditional network generation model to generate conditions based on target categories and frequency bands, providing high fidelity samples for SAR deception jamming.

Dynamic change of a typical surging glacier in the Qinghai tibet plateau based on satellite remote sensing

With global climate change, some mountain glaciers in the Qinghai–Tibet Plateau region are experiencing rapid melting, retreating, and acceleration. Surge-type glaciers are important objects for glacier dynamics research in this region, as sudden surge may threaten human activities and lives. This study focuses on the Shahrek Glacier. Based on multi-source remote sensing images from 2000 to 2023, we generated long-term ice flow velocity maps of the glacier using image template matching technology. Together with its terminus position over this period, the surge dynamics of Shahrek Glacier were analyzed. The glacier surge commenced in September 2016 and terminated in November 2018, reaching a maximum ice velocity of almost 1,200 m/a, which is 9–10 times its ice velocity in the quiescent phase. A substantial change in the magnitude of the ice flow before and after the onset of the Shahrek Glacier surge was observed; the surge comprised five main phases: pre-surge stable, accelerated, surge, decelerated, and stabilized periods. The Shahrek Glacier was retreating from the position of the glacier-terminal ablation zone prior to the onset of leapfrogging. We analyzed the relationship between Shahrek Glacier dynamics and climate and the surge mechanism surge, considering data from meteorological stations in neighboring regions.

Highly compact adaptive network based on transformer for RGBT tracking

RGBT tracking is a challenging task that requires robust fusion of visible images (RGB) and thermal infrared images (TIR) to handle various scenarios, such as illumination changes, occlusions, and camouflage. The current popular RGBT trackers mostly based on two stream Siamese trackers. They tend to separately extracts template and search images region features, which neglects relationship between target and background. Moreover, they do not fuse RGB and TIR features properly, limiting their ability to utilize complementary features. To address these issues, we introduce a highly compact adaptive transformer-based network that unifies the process of feature extracting and correlation between template and search images. In this way, the compact network has dual-branches. They can combine feature extraction and correlation for different modalities of template images and search images. Meanwhile, we introduce a cross-modal weight redistribution module (CMWR) for multi-modal fusion. This adaptive fusion scheme learns discriminative features of RGB and TIR data and assigns weights to them, enabling them to complement each other. Furthermore, to address the issue of tracking targets of different scales, we design scale-adaptive optimization pyramid module (SAOP) that adapt to objects of different sizes. Our method achieves exceptional performance on the GTOT, RGBT234 and LasHeR datasets, surpassing most of the existing methods. The results are consistent across multiple datasets, demonstrating the effectiveness and superiority of our approach. And our code is released at: https://github.com/ELOESZHANG/HCANet.

Efficient in-site laser scanning scheme with adaptive angular resolution for long-span bridge geometry measurement

For geometrical measurements on long-span bridges, Terrestrial Laser Scanning (TLS) using conventional scanning approach yields the model with over-dense points in close regions and point redundancy in background. This not only hampers the efficiency of on-site scanning but also complicates data post-processing. This study introduces an optimised scanning scheme incorporating automatic identification of bridge key components and fine scan planning with adaptable angular resolutions. A novel method for TLS pose calibration, leveraging geometric feature partitioning and image template matching is proposed, which aids in projecting bridge component labels from the design model to the scan model in the TLS’s view. Considering the substantial disparities in scanning distances across different regions, a multi-segment scan planning is proposed through the clustering of spherical angles and desired angular resolutions. The effectiveness of the proposed method is demonstrated through a field test on a long-span bridge under construction, specifically for deck height measurements.

Dynamic estimation method for pulsar periods based on photon energy distribution folding and image template matching

The accuracy of the pulsar period estimation directly affects the restoration effect of the signal profile. A more accurate pulsar profile will help improve the accuracy of pulsar delay estimation and thereby improve the performance of X-ray pulsar navigation. This paper proposes a pulsar period estimation method based on photon energy distribution folding and image template matching (PETM). This method uses the probability distribution information of photon energy for weighted epoch folding. The one-dimensional (1D) profile information was converted into two-dimensional (2D) image information through reverse space-filling curve (SFC) encoding. Then, a feature matching was performed between the target structure and the template structure. At the same time, the criterion of Pearson correlation coefficient (PCC) was used to quantitatively evaluate the matching effect to estimate the optimal period. The simulation results show that the period estimation accuracy of the PETM method is significantly improved, as compared with the traditional \( method. This work also analyzes the folding effect based on the photon energy distribution model and conducts simulation experiments and comparisons on influencing factors, such as noise interference and data quality. At the same time, we also specifically demonstrated the effectiveness of the PETM method for the glitch phenomenon (i.e., a sudden change in period) of pulsar periods. Finally, we also used China's XPNAV-1 satellite to conduct experiments and analysis of the actual observation data of PSR B0531+21 pulsar within a fixed period of time. The results show that the period estimation accuracy of this method is 4.8190$ns$, which is 50.23&lt;!PCT!&gt; higher than the traditional \( method. The method proposed in this article has the advantages of high estimation accuracy, stable estimation performance, strong anti-interference ability, and excellent dynamic period estimation performance. Therefore, it can further improve the navigation performance of X-ray pulsars.

Sequential Fusion Based Multi-Granularity Consistency for Space-Time Transformer Tracking

Regarded as a template-matching task for a long time, visual object tracking has witnessed significant progress in space-wise exploration. However, since tracking is performed on videos with substantial time-wise information, it is important to simultaneously mine the temporal contexts which have not yet been deeply explored. Previous supervised works mostly consider template reform as the breakthrough point, but they are often limited by additional computational burdens or the quality of chosen templates. To address this issue, we propose a Space-Time Consistent Transformer Tracker (STCFormer), which uses a sequential fusion framework with multi-granularity consistency constraints to learn spatiotemporal context information. We design a sequential fusion framework that recombines template and search images based on tracking results from chronological frames, fusing updated tracking states in training. To further overcome the over-reliance on the fixed template without increasing computational complexity, we design three space-time consistent constraints: Label Consistency Loss (LCL) for label-level consistency, Attention Consistency Loss (ACL) for patch-level ROI consistency, and Semantic Consistency Loss (SCL) for feature-level semantic consistency. Specifically, in ACL and SCL, the label information is used to constrain the attention and feature consistency of the target and the background, respectively, to avoid mutual interference. Extensive experiments have shown that our STCFormer outperforms many of the best-performing trackers on several popular benchmarks.