Papers Visual Tracking
“Visual Tracking” 태그가 달린 논문 581편 · 필터 해제
Distilling Localization for Self-Supervised Representation Learning
Recent progress in contrastive learning has revolutionized unsupervised representation learning. Concretely, multiple views (augmentations) from the same image are encouraged to map to the similar embeddings, while views…
ColorizationContrastive LearningGeneral Classificationobject-detection+6Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking
In recent years, visual tracking methods that are based on discriminative correlation filters (DCF) have been very promising. However, most of these methods suffer from a lack of robust scale estimation skills. Although …
Computational EfficiencyVisual TrackingBeyond Background-Aware Correlation Filters: Adaptive Context Modeling by Hand-Crafted and Deep RGB Features for Visual Tracking
In recent years, the background-aware correlation filters have achie-ved a lot of research interest in the visual target tracking. However, these methods cannot suitably model the target appearance due to the exploitatio…
Semantic SimilaritySemantic Textual SimilarityvalidVisual TrackingEffective Fusion of Deep Multitasking Representations for Robust Visual Tracking
Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative c…
Object TrackingSemantic SegmentationVisual Object TrackingVisual TrackingHigh-Performance Long-Term Tracking with Meta-Updater
Long-term visual tracking has drawn increasing attention because it is much closer to practical applications than short-term tracking. Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, t…
Visual Object TrackingVisual TrackingVocal Bursts Intensity PredictionProgressive Multi-Stage Learning for Discriminative Tracking
Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples c…
Visual TrackingAutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization
Most existing trackers based on discriminative correlation filters (DCF) try to introduce predefined regularization term to improve the learning of target objects, e.g., by suppressing background learning or by restricti…
CPUGPUVisual TrackingProbabilistic Regression for Visual Tracking
Visual tracking is fundamentally the problem of regressing the state of the target in each video frame. While significant progress has been achieved, trackers are still prone to failures and inaccuracies. It is therefore…
Object TrackingregressionVisual TrackingSiamese Box Adaptive Network for Visual Tracking
Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target. Unfortunately, they typically call for tediou…
Visual TrackingKeyfilter-Aware Real-Time UAV Object Tracking
Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency. However, it has two imperfections, i.e., boundary effect and filter corruption. Several methods enlarging t…
ObjectObject TrackingSimultaneous Localization and MappingVisual TrackingEfficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge
Compared to rigid robots that are generally studied in reinforcement learning, the physical characteristics of some sophisticated robots such as soft or continuum robots are higher complicated. Moreover, recent reinforce…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Visual TrackingObject-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation
In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classification-b…
Computational EfficiencyData AugmentationGeneral ClassificationGenerative Adversarial Network+2An Analysis of Object Representations in Deep Visual Trackers
Fully convolutional deep correlation networks are integral components of state-of the-art approaches to single object visual tracking. It is commonly assumed that these networks perform tracking by detection by matching …
ObjectSaliency DetectionVisual TrackingGeneralized mean shift with triangular kernel profile
The mean shift algorithm is a popular way to find modes of some probability density functions taking a specific kernel-based shape, used for clustering or visual tracking. Since its introduction, it underwent several pra…
ClusteringVisual TrackingRobust Visual Tracking via Implicit Low-Rank Constraints and Structural Color Histograms
With the guaranteed discrimination and efficiency of spatial appearance model, Discriminative Correlation Filters (DCF-) based tracking methods have achieved outstanding performance recently. However, the construction of…
Visual TrackingSiamMan: Siamese Motion-aware Network for Visual Tracking
In this paper, we present a novel siamese motion-aware network (SiamMan) for visual tracking, which consists of the siamese feature extraction subnetwork, followed by the classification, regression, and localization bran…
General ClassificationregressionVisual TrackingOcclusion-Robust Online Multi-Object Visual Tracking using a GM-PHD Filter with CNN-Based Re-Identification
We propose a novel online multi-object visual tracker using a Gaussian mixture Probability Hypothesis Density (GM-PHD) filter and deep appearance learning. The GM-PHD filter has a linear complexity with the number of obj…
Large-Scale Person Re-IdentificationMultiple Object TrackingObject TrackingPerson Re-Identification+1Kernel learning for visual perception
The visual perceptual system in animals allows them to assimilate information from their surroundings. In artificial intelligence, the objective of visual perception is to enable the capability of a computer system to in…
Activity RecognitionHuman Activity RecognitionLoop Closure Detectionobject-detection+8Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers
We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task. The proposed SNLT is applicable to a wide range of …
GPUObject TrackingRegion ProposalVisual Object Tracking+1ATIS + SpiNNaker: a Fully Event-based Visual Tracking Demonstration
The Asynchronous Time-based Image Sensor (ATIS) and the Spiking Neural Network Architecture (SpiNNaker) are both neuromorphic technologies that "unconventionally" use binary spikes to represent information. The ATIS prod…
CPUVisual Tracking