Visual object tracking, pivotal for applications like earth observation and environmental monitoring, encounters challenges under adverse conditions such as low light and complex backgrounds. Traditional tracking technologies often falter, especially...
Offline-trained Siamese networks are not robust to the environmental complication in visual object tracking. Without online learning, the Siamese network cannot learn from instance domain knowledge and adapt to appearance changes of targets. In this...
Visual object tracking is a fundamental computer vision task recently extended to multimodal settings, where natural language descriptions complement visual information. Existing multimodal trackers typically rely on large-scale transformer architect...
The capability of unmanned aerial vehicles (UAVs) to capture and utilize dynamic object information assumes critical significance for decision making and scene understanding. This paper presents a method for UAV relative positioning and target tracki...
Object information significantly affects the performance of visual tracking. However, it is difficult to obtain accurate target foreground information because of the existence of challenging scenarios, such as occlusion, background clutter, drastic c...
With the rapid development of autonomous driving and robotics applications in recent years, visual Simultaneous Localization and Mapping (SLAM) has become a hot research topic. The majority of visual SLAM systems relies on the assumption of scene rig...
In visual object tracking fields, the Siamese network tracker, based on the region proposal network (SiamRPN), has achieved promising tracking effects, both in speed and accuracy. However, it did not consider the relationship and differences between...
Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking...
Visual object tracking is still considered a challenging task in computer vision research society. The object of interest undergoes significant appearance changes because of illumination variation, deformation, motion blur, background clutter, and oc...
Siamese network-based trackers are broadly applied to solve visual tracking problems due to its balanced performance in terms of speed and accuracy. Tracking desired objects in challenging scenarios is still one of the fundamental concerns during vis...
Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms have evolved in recent years as a result of deep learning’s outs...
With the rise of general models, transformers have been adopted in visual object tracking algorithms as feature fusion networks. In these trackers, self-attention is used for global feature enhancement. Cross-attention is applied to fuse the features...
The visual object tracking problem seeks to track an arbitrary object in a video, and many deep convolutional neural network-based algorithms have achieved significant performance improvements in recent years. However, most of them do not guarantee r...
As a classic framework for visual object tracking, the Siamese convolutional neural network has received widespread attention from the research community. This method uses a convolutional neural network to obtain the object features and to match them...
In this paper, we present a novel onboard robust visual algorithm for long-term arbitrary 2D and 3D object tracking using a reliable global-local object model for unmanned aerial vehicle (UAV) applications, e.g., autonomous tracking and chasing a mov...
Visual tracking task is divided into classification and regression tasks, and manifold features are introduced to improve the performance of the tracker. Although the previous anchor-based tracker has achieved superior tracking performance, the ancho...
Visual object tracking for unmanned aerial vehicles (UAV) is widely used in many fields such as military reconnaissance, search and rescue work, film shooting, and so on. However, the performance of existing methods is still not very satisfactory due...
Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrup...
Siamese networks have been extensively studied in recent years. Most of the previous research focuses on improving accuracy, while merely a few recognize the necessity of reducing parameter redundancy and computation load. Even less work has been don...
The visual object tracking technology of remote sensing images has important applications in areas with high safety performance such as national defense, homeland security, and intelligent transportation in smart cities. However, previous research de...
Adversarial attacks on visual object tracking aim to degrade tracking accuracy by introducing imperceptible perturbations into video frames, exploiting vulnerabilities in neural networks. In real-world symmetrical double-blind engagements, both attac...
Recently, Discriminative Correlation Filters (DCF) have shown excellent performance in visual object tracking. The correlation for a computing response map can be conducted efficiently in Fourier domain by Discrete Fourier Transform (DFT) of inputs,...
Visual object tracking technology is one of the key issues in computer vision. In this paper, we propose a visual object tracking algorithm based on cross-modality featuredeep learning using Gaussian-Bernoulli deep Boltzmann machines (DBM) with RGB-D...
Real-time visual object tracking (VOT) may suffer from performance degradation and even divergence owing to inaccurate noise statistics typically engendered by non-stationary video sequences or alterations in the tracked object. This paper presents a...
With the rapid development of deep learning techniques, new breakthroughs have been made in deep learning-based object tracking methods. Although many approaches have achieved state-of-the-art results, existing methods still cannot fully satisfy prac...
Most visual simultaneous localization and mapping (SLAM) systems are based on the assumption of a static environment in autonomous vehicles. However, when dynamic objects, particularly vehicles, occupy a large portion of the image, the localization a...
Query decoders have been shown to achieve good performance in object detection. However, they suffer from insufficient object tracking performance. Sequence-to-sequence learning in this context has recently been explored, with the idea of describing...
Recently, Siamese architecture has been widely used in the field of visual tracking, and has achieved great success. Most Siamese network based trackers aggregate the target information of two branches by cross-correlation. However, since the locatio...
To demonstrate that the near-point-of-convergence (npc) examination is performed subjectively, the visual fixation and four-prism diopter (Δ) base-out (BO) examinations can be conducted with a kiosk-type objective examination method based on ga...
Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D...
Visual object tracking is an important research topic in the field of computer vision. Tracking–learning–detection (TLD) decomposes the tracking problem into three modules—tracking, learning, and detection—which provides effec...
In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs) to detect, track and recognize in real time objects encountered during navigation in the outdo...
One of the most important and challenging research subjects in computer vision is visual object tracking. The information obtained from the first frame consists of limited and insufficient information to represent an object. If prior information abou...
Visual object tracking is a fundamental task in computer vision, with applications ranging from video surveillance to autonomous driving. Despite recent advances in transformer-based one-stream trackers, unrestricted feature interactions between the...
In most visual tracking tasks, the target is tracked by a bounding box given in the first frame. The complexity and redundancy of background information in the bounding box inevitably exist and affect tracking performance. To alleviate the influence...
To balance the real-time and robustness of UAV visual tracking on a single CPU, this paper proposes an object tracker based on spatio-temporal context (STCT). STCT integrates the correlation filter and Siamese network into a unified framework and int...
In this paper, we propose a novel visual tracking method with a object part-aware attention-based matching (OPAM) mechanism, which leverages local–global attention to enhance visual tracking performance. Our method introduces three key componen...
Anchor-free-based trackers have achieved remarkable performance in single visual object tracking in recent years. Most anchor-free trackers consider the rectangular fields close to the target center as the positive sample used in the training phase,...
In this paper, we propose a novel visual tracking method for unmanned aerial vehicles (UAVs) in aerial scenery. To track the UAVs robustly, we present a new object proposal method that can accurately determine the object regions that are likely to ex...
Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of mul...
With the development of computer vision and Unmanned Aerial Vehicles (UAVs) technology, visual object tracking has become an indispensable core technology for UAVs, and it has been widely used in both civil and military fields. Visual object tracking...
Target detection and tracking algorithms are one of the key technologies in the field of autonomous driving in intelligent transportation, providing important sensing capabilities for vehicle localization and path planning. Siamese network-based trac...
We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feat...
We propose to improve the visual object tracking by introducing a soft mask based low-level feature fusion technique. The proposed technique is further strengthened by integrating channel and spatial attention mechanisms. The proposed approach is int...
The Discriminative Correlation Filter (DCF) has been universally recognized in visual object tracking, thanks to its excellent accuracy and high speed. Nevertheless, these DCF-based trackers perform poorly in long-term tracking. The reasons include t...
Correlation filter (CF) based trackers have gained significant attention in the field of visual single-object tracking, owing to their favorable performance and high efficiency; however, existing trackers still suffer from model drift caused by bound...
Although there have been recent advances in Siamese-network-based visual tracking methods where they show high performance metrics on numerous large-scale visual tracking benchmarks, persistent challenges regarding the distractor objects with similar...
This paper explores the spatial coverage potential of a vision sensor to implement a dual service for a museum security system. Services include object monitoring and invisible visual Multiple-Input and Multiple-Output (MIMO) communication. For objec...
Visual tracking has become widespread in swarm robots for intelligent video surveillance, navigation, and autonomous vehicles due to the development of machine learning algorithms. Discriminative correlation filter (DCF)-based trackers have gained in...
Multi-object tracking (MOT) is an important task in computer vision, particularly in complex, dynamic environments with crowded scenes and frequent occlusions. Traditional tracking methods often suffer from identity switches (IDSws) and fragmented tr...
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