Papers Visual Object Tracking
“Visual Object Tracking” 태그가 달린 논문 369편 · 필터 해제
Efficient universal shuffle attack for visual object tracking
Recently, adversarial attacks have been applied in visual object tracking to deceive deep trackers by injecting imperceptible perturbations into video frames. However, previous work only generates the video-specific pert…
Adversarial AttackComputational EfficiencyObjectObject Tracking+1Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking
Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prio…
AllObject TrackingVisual Object TrackingCorrelation-Aware Deep Tracking
Robustness and discrimination power are two fundamental requirements in visual object tracking. In most tracking paradigms, we find that the features extracted by the popular Siamese-like networks cannot fully discrimina…
Feature CorrelationObject TrackingVisual Object TrackingGlobal Instance Tracking: Locating Target More Like Humans
Target tracking, the essential ability of the human visual system, has been simulated by computer vision tasks. However, existing trackers perform well in austere experimental environments but fail in challenges like occ…
Visual Object TrackingVisual TrackingNetwork Comparison Study of Deep Activation Feature Discriminability with Novel Objects
Feature extraction has always been a critical component of the computer vision field. More recently, state-of-the-art computer visions algorithms have incorporated Deep Neural Networks (DNN) in feature extracting roles, …
ObjectObject TrackingVisual Object TrackingFew-Shot Backdoor Attacks on Visual Object Tracking
Visual object tracking (VOT) has been widely adopted in mission-critical applications, such as autonomous driving and intelligent surveillance systems. In current practice, third-party resources such as datasets, backbon…
Autonomous DrivingBackdoor AttackObjectObject Tracking+1You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
Promising results have been achieved recently in category-level manipulation that generalizes across object instances. Nevertheless, it often requires expensive real-world data collection and manual specification of sema…
3D Object TrackingIndustrial RobotsObjectPose Tracking+4A Survey for Deep RGBT Tracking
Visual object tracking with the visible (RGB) and thermal infrared (TIR) electromagnetic waves, shorted in RGBT tracking, recently draws increasing attention in the tracking community. Considering the rapid development o…
Object TrackingSurveyVisual Object TrackingVisual Object Tracking on Multi-modal RGB-D Videos: A Review
The development of visual object tracking has continued for decades. Recent years, as the wide accessibility of the low-cost RGBD sensors, the task of visual object tracking on RGB-D videos has drawn much attention. Comp…
BenchmarkingObjectObject TrackingVisual Object TrackingTemporal Aggregation for Adaptive RGBT Tracking
Visual object tracking with RGB and thermal infrared (TIR) spectra available, shorted in RGBT tracking, is a novel and challenging research topic which draws increasing attention nowadays. In this paper, we propose an RG…
Object TrackingVisual Object TrackingExploring Fusion Strategies for Accurate RGBT Visual Object Tracking
We address the problem of multi-modal object tracking in video and explore various options of fusing the complementary information conveyed by the visible (RGB) and thermal infrared (TIR) modalities including pixel-level…
ObjectObject TrackingVisual Object TrackingA Discriminative Single-Shot Segmentation Network for Visual Object Tracking
Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of transformation models, which reduces their l…
ObjectObject TrackingSegmentationSemantic Segmentation+3Efficient Visual Tracking with Exemplar Transformers
The design of more complex and powerful neural network models has significantly advanced the state-of-the-art in visual object tracking. These advances can be attributed to deeper networks, or the introduction of new bui…
CPUObject TrackingVideo Object TrackingVisual Object Tracking+1FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
We present FEAR, a family of fast, efficient, accurate, and robust Siamese visual trackers. We present a novel and efficient way to benefit from dual-template representation for object model adaption, which incorporates …
Visual Object TrackingVisual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook
Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location…
Object TrackingSurveyVisual Object TrackingVisual TrackingSwinTrack: A Simple and Strong Baseline for Transformer Tracking
Recently Transformer has been largely explored in tracking and shown state-of-the-art (SOTA) performance. However, existing efforts mainly focus on fusing and enhancing features generated by convolutional neural networks…
Representation LearningVisual Object TrackingVisual TrackingImplicit Equivariance in Convolutional Networks
Convolutional Neural Networks(CNN) are inherently equivariant under translations, however, they do not have an equivalent embedded mechanism to handle other transformations such as rotations and change in scale. Several …
Object TrackingVisual Object TrackingLearning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking
Recently, template-based trackers have become the leading tracking algorithms with promising performance in terms of efficiency and accuracy. However, the correlation operation between query feature and the given templat…
Object TrackingSegmentationState EstimationVisual Object Tracking+1Explicitly Modeling the Discriminability for Instance-Aware Visual Object Tracking
Visual object tracking performance has been dramatically improved in recent years, but some severe challenges remain open, like distractors and occlusions. We suspect the reason is that the feature representations of the…
Contrastive LearningObject TrackingVisual Object TrackingVisual TrackingDepth-only Object Tracking
Depth (D) indicates occlusion and is less sensitive to illumination changes, which make depth attractive modality for Visual Object Tracking (VOT). Depth is used in RGBD object tracking where the best trackers are deep R…
ObjectObject TrackingVisual Object Tracking