Di Yuan

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Verified

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• Doctor of Engineering
• Lecturer at Xidian University

Due to the lack of large-scale labeled Thermal InfraRed (TIR) training datasets, most existing TIR trackers are trained directly on RGB datasets. However, tracking methods trained on RGB datasets suffer a significant drop-off in TIR data, due to the domain shift issue. To address this issue, we propose a Progressive Domain Adaptation framework for...

Most extant underwater object tracking (UOT) utilize generic tracking algorithms, which lack applicability to underwater tracking scenarios. Moreover, these algorithms primarily emphasize minimizing interference from various challenging tasks to prevent target drift, but pay less attention to the strategies for mitigating target drift once it occur...

Thermal infrared (TIR) object tracking is a significant subject within the field of computer vision. Currently, TIR object tracking faces challenges such as insufficient representation of object texture information and underutilization of temporal information, which severely affects the tracking accuracy of TIR tracking methods. To address these is...

The limited availability of thermal infrared (TIR) training samples leads to suboptimal target representation by convolutional feature extraction networks, which adversely impacts the accuracy of TIR target tracking methods. To address this issue, we propose an unsupervised cross-domain model (UCDT) for TIR tracking. Our approach leverages labeled...

Due to the lack of large-scale labeled Thermal InfraRed (TIR) training datasets, most existing TIR trackers are trained directly on RGB datasets. However, tracking methods trained on RGB datasets suffer a significant drop-off in TIR data due to the domain shift issue. To this end, in this work, we propose a Progressive Domain Adaptation framework f...

Most of the existing infrared and visible image fusion algorithms rely on hand-designed or simple convolution-based fusion strategies. However, these methods cannot explicitly model the contextual relationships between infrared and visible images, thereby limiting their robustness. To this end, we propose a novel Transformer-based feature fusion ne...

Visual object tracking has attracted much attention thanks to its remarkable capability to identify a moving target accurately in real-world video scenarios. Recently, tracking performance has improved significantly. However, there is still a lot of progress space to achieve consummate tracking performance because of the complex and varied target a...

Road extraction from remote sensing images has attracted widespread attention of researchers due to its crucial role in the fields of autopilot, urban planning, navigation, and other fields. However, the task becomes challenging as the roads in remote sensing images are easily occluded by obstacles such as shadows, buildings and trees. In this lett...

RGBT tracking seeks to leverage both visible and thermal infrared images to enhance the robustness of target tracking. This method makes up for the limitations of single-sensor tracking. The RGB and thermal infrared images complement each other effectively, enabling the tracker to operate seamlessly in complex environments day and night. However, R...

Thermal infrared (TIR) target tracking is an important topic in the computer vision area. The TIR images are not affected by ambient light and have strong environmental adaptability, making them widely used in battlefield perception, video surveillance, assisted driving, etc. However, TIR target tracking faces problems such as relatively insufficie...

The discriminative model prediction (DiMP) object tracking model is an excellent end-to-end tracking framework and have achieved the best results of its time. However, there are two problems with DiMP in the process of actual use: (1) DiMP is prone to interference from similar objects during the tracking process, and (2) DiMP requires a large amoun...

Thermal infrared (TIR) target tracking task is not affected by illumination changes and can be tracked at night, on rainy days, foggy days, and other extreme weather, so it is widely used in auxiliary driving, unmanned aerial vehicle reconnaissance, video surveillance, and other scenes. However, the TIR target tracking task also presents some chall...

With the continuous advancement of autonomous driving technology, 3D vehicle detection has become of widespread interest. The traditional aggregate view object detection (AVOD) framework has achieved some good results in 3D vehicle detection tasks. However, the complexity of the 3D vehicle detection scenario makes the current detection methods stil...

Unlike visual object tracking, thermal infrared (TIR) object tracking methods can track the target of interest in poor visibility such as rain, snow, and fog, or even in total darkness. This feature brings a wide range of application prospects for TIR object-tracking methods. However, this field lacks a unified and large-scale training and evaluati...

Thermal infrared (TIR) target tracking task is not affected by illumination changes and can be tracked at night, on rainy days, foggy days, and other extreme weather, so it is widely used in night auxiliary driving, unmanned aerial vehicle reconnaissance, video surveillance, and other scenes. Thermal infrared target tracking task still faces many c...

Thermal infrared (TIR) target tracking is susceptible to occlusion and similarity interference, which obviously affects the tracking results. To resolve this problem, we develop an Aligned Spatial-Temporal Memory network-based Tracking method (ASTMT) for the TIR target tracking task. Specifically, we model the scene information in the TIR target tr...

When dealing with complex thermal infrared (TIR) tracking scenarios, the single category feature is not sufficient to portray the appearance of the target, which drastically affects the accuracy of the TIR target tracking method. In order to address these problems, we propose an adaptively multi-feature fusion model (AMFT) for the TIR tracking task...

Thermal InfraRed (TIR) target trackers are easy to be interfered by similar objects, while susceptible to the influence of the target occlusion. To solve these problems, we propose a structural target-aware model (STAMT) for the thermal infrared target tracking tasks. Specifically, the proposed STAMT tracker can learn a target-aware model, which ca...

Follow-up experimental designs are widely used in various scientific investigations and industrial applications to discuss the relationship between inputs and outputs at various stages. Due to the limitation of run size, the desired experimental purpose may not be achieved through one stage of the experiment, so additional runs should be considered...

Most existing trackers are based on using a classifier and multi-scale estimation to estimate the target state. Consequently, and as expected, trackers have become more stable while tracking accuracy has stagnated. While trackers adopt a maximum overlap method based on an intersection-over-union (IoU) loss to mitigate this problem, there are defect...

The feature models used by existing Thermal InfraRed (TIR) tracking methods are usually learned from RGB images due to the lack of a large-scale TIR image training dataset. However, these feature models are less effective in representing TIR objects and they are difficult to effectively distinguish distractors because they do not contain fine-grain...

The current Siamese network based on region proposal network (RPN) has attracted great attention in visual tracking due to its excellent accuracy and high efficiency. However, the design of the RPN involves the selection of the number, scale, and aspect ratios of anchor boxes, which will affect the applicability and convenience of the model. Furthe...

Existing infrared and visible image fusion methods usually use hand-designed or simple convolution based fusion strategies which cannot model the contextual relationships between infrared and visible images explicitly. To this end, in this paper, we propose a Transformer based feature fusion network to model the contextual relationship of the two m...

Integrating multi-feature based on multi-layer features from the convolutional network or based on multiple hand-crafted features has been proved to be an effective way for improving tracking performance. In this work, we investigate how to integrate multi-layer convolutional features with hand-crafted features. Specifically, an adaptive multi-feat...

Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled training samples, and the number and quality of these samples directly affect the representational capability of the trained model. However, this approach is restricti...

Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target tracking tasks. Different from the target tracking task in the general scenarios, the target tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the DCFs-based tracker has achieved good...

The current Siamese network based on region proposal network (RPN) has attracted great attention in visual tracking due to its excellent accuracy and high efficiency. However, the design of the RPN involves the selection of the number, scale, and aspect ratios of anchor boxes, which will affect the applicability and convenience of the model. Furthe...

The training of a feature extraction network typically requires abundant manually annotated training samples, making this a time-consuming and costly process. Accordingly, we propose an effective self-supervised learning-based tracker in a deep correlation framework (named: self-SDCT). Motivated by the forward-backward tracking consistency of a rob...

In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and more than 600K frames. We annotate the bounding box of objects in every frame of all sequences and generate over...

In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTB-TIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and more than 600K frames. We annotate the bounding box of objects in every frame of all sequences and generate over...

While part-based methods have been shown effective in the person re-identification task, it is unreasonable for most of them to treat each part equally, due to the retrieved image may be affected by deformation, occlusion and other factors, which makes the feature information of some parts unreliable. Instead of using the same weight of each part f...

Correlation filter-based trackers (CFTs) have recently shown remarkable performance in the field of visual object tracking. The advantage of these trackers originates from their ability to convert time-domain calculations into frequency domain calculations. However, a significant problem of these CFTs is that the model is insufficiently robust when...

Existing deep Thermal InfraRed (TIR) trackers only use semantic features to represent the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images. To address this issue, we propose a multi-level similarity model under a Siamese frame...

Most existing tracking methods are based on using a classifier and multi-scale estimation to estimate the state of the target. Consequently, and as expected, trackers have become more stable while tracking accuracy has stagnated. While the ATOM \cite{ATOM} tracker adopts a maximum overlap method based on an intersection-over-union (IoU) loss to mit...

Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific di...

Discriminative correlation filters (DCFs) have been widely used in the tracking community recently. DCFs-based trackers utilize samples generated by circularly shifting from an image patch to train a ridge regression model, and estimate target location using a response map generated by the correlation filters. However, the generated samples produce...

Convolutional Neural Networks (CNN) have been demonstrated to achieve state-of-the-art performance in visual object tracking task. However, existing CNN-based trackers usually use holistic target samples to train their networks. Once the target undergoes complicated situations (e.g., occlusion, background clutter, and deformation), the tracking per...

Discriminative correlation filters (DCFs) have been widely used in the visual tracking community in recent years. The DCFs-based trackers determine the target location through a response map generated by the correlation filters and determine the target scale by a fixed scale factor. However, the response map is vulnerable to noise interference and...

The Vision Meets Drone (VisDrone2020) Single Object Tracking is the third annual UAV tracking evaluation activity organized by the VisDrone team, in conjunction with European Conference on Computer Vision (ECCV 2020). The VisDrone-SOT2020 Challenge presents and discusses the results of 13 participating algorithms in detail. By using ensemble of dif...

Multi-Task Driven Feature Models for Thermal Infrared Tracking--Supplementary Materials

Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However , these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific d...

Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific di...

Common tracking algorithms only use a single feature to describe the target appearance, which makes the appearance model easily disturbed by noise. Furthermore, the tracking performance and robustness of these trackers are obviously limited. In this paper, we propose a novel multiple feature fused model into a correlation filter framework for visua...

In the field of engineering technology, many problems can be transformed into the first kind Fredholm integral equation, which has a prominent feature called “ill-posedness”. This property makes it difficult to find the analytical solution of first kind Fredholm integral equation. Therefore, how to find the numerical solution of first kind Fredholm...

Existing deep Thermal InfraRed (TIR) trackers only use semantic features to describe the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images.To address this issue, we propose a multi-level similarity model under a Siamese framewo...

Most of the correlation filter based tracking algorithms can achieve good performance and maintain fast computational speed. However, in some complicated tracking scenes, there is a fatal defect that causes the object to be located inaccurately, which is the trackers excessively dependent on the maximum response value to determine the object locati...

Graph-based methods have been widely applied in clustering problems. The mainstream pipeline for these methods is to build an affinity matrix first, and then use the spectral clustering methods to construct a graph. The existing studies about such a pipeline mainly focus on how to build a good affinity matrix, while the spectral method has only bee...

To overcome the premature problem of the traditional flower pollination algorithm， a novel niche flower pollination algorithm is proposed by combining the niche strategy with flower pollination algorithm． It is designed for the parameter inversion of the space fractional order diffusion equation， so as to provide some theoretical basis for the poll...

In the most tracking approaches, a score function is utilized to determine which candidate is the optimal one by measuring the similarity between the candidate and the template. However, the representative samples selection in the template update is challenging. To address this problem, in this paper, we treat the template as a linear combination o...

The accurate localization of a object target is a challenging research issue in visual tracking. Most correlation filter based tracking algorithms has been degraded their performances because of the weaknesses of their search strategy. This paper investigates the problem of accurate location the object target in visual tracking sequences. We propos...

Robust visual object tracking is one of the most challenging issues in the field of computer vision. Because of the circular shifts strategy, correlation filter-based trackers show a great efficiency in tracking task and thus receive lots of attentions. However, most of the correlation filter-based trackers fix the scale of the targets in each fram...

The general tracking algorithm is vulnerable to noise because of using a single feature, makes the performance and robustness of the those algorithms greatly limited. In this paper, in order to achieve the robust and pretty performance, we propose a novel multiple feature fused model in correlation filter framework for visual tracking. The adoption...

In this paper, we propose a novel thermal infrared (TIR) tracker via a deep Siamese convolutional neural network (CNN), named Siamese tir. Different from the most existing discriminative TIR tracking methods which treat the tracking problem as a classification problem, we treat the TIR tracking problem as a similarity verification problem. Specific...

Most of the correlation filter based tracking algorithms can achieve good performance and maintain fast computational speed. However, in some complicated tracking scenes, there is a fatal defect that causes the object to be located inaccurately. In order to address this problem, we propose a particle filter redetection based tracking approach for a...

In this paper, based on the space fractional order diffusion equation, we estimate the equation parameters by using an improved ant colony algorithm, that is, the Niche Ant Colony Algorithm (NACA) based on fitness sharing principle. Its efficiency is verified by application of 20 standard test functions of 1-20 variables compared with standard ant...

Occlusion is one of the most challenging problems in visual object tracking. Recently, a lot of discriminative methods have been proposed to deal with this problem. For the discriminative methods, it is difficult to select the representative samples for the target template updating. In general, the holistic bounding boxes that contain tracked resul...

The paper analyses the dynamic relationship between the quantity of employment in three major industries and GDP in Hunan Province from 1952 to 2014 through VAR model. The result shows that there exists the bidirectional Granger Causality relationship between the GDP growth and the quantity of employment in Hunan Province. The development of GDP ha...

Some nature of the skew commutative matrix was given in this paper. And some necessary and sufficient conditions about some skew commutative matrixes were given．