Papers Visual Tracking
“Visual Tracking” 태그가 달린 논문 581편 · 필터 해제
Pyramid-based Visual Tracking Using Sparsity Represented Mean Transform
In this paper, we propose a robust method for visual tracking relying on mean shift, sparse coding and spatial pyramids. Firstly, we extend the original mean shift approach to handle orientation space and scale space and…
Visual TrackingPartial Occlusion Handling for Visual Tracking via Robust Part Matching
Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challengi…
Occlusion HandlingSparse LearningVisual TrackingVisual Tracking via Probability Continuous Outlier Model
In this paper, we present a novel online visual tracking method based on linear representation. First, we present a novel probability continuous outlier model (PCOM) to depict the continuous outliers that occur in the li…
modelVisual TrackingVisual Tracking Using Pertinent Patch Selection and Masking
A novel visual tracking algorithm using patch-based appearance models is proposed in this paper. We first divide the bounding box of a target object into multiple patches and then select only pertinent patches, which occ…
PositionVisual TrackingInterval Tracker: Tracking by Interval Analysis
This paper proposes a robust tracking method that uses interval analysis. Any single posterior model necessarily includes a modeling uncertainty (error), and thus, the posterior should be represented as an interval of pr…
Visual TrackingObject Tracking via Non-Euclidean Geometry: A Grassmann Approach
A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained u…
ObjectObject TrackingVisual TrackingVisual Tracking using Particle Swarm Optimization
The problem of robust extraction of visual odometry from a sequence of images obtained by an eye in hand camera configuration is addressed. A novel approach toward solving planar template based tracking is proposed which…
RetrievalVisual OdometryVisual TrackingLearning a Deep Compact Image Representation for Visual Tracking
In this paper, we study the challenging problem of tracking the trajectory of a moving object in a video with possibly very complex background. In contrast to most existing trackers which only learn the appearance of th…
DenoisingGeneral ClassificationObjectTransfer Learning+1Fast Tracking via Spatio-Temporal Context Learning
In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest an…
PositionVisual TrackingOnline Unsupervised Feature Learning for Visual Tracking
Feature encoding with respect to an over-complete dictionary learned by unsupervised methods, followed by spatial pyramid pooling, and linear classification, has exhibited powerful strength in various vision applications…
Dictionary LearningVisual TrackingVisual Tracking via Locality Sensitive Histograms
This paper presents a novel locality sensitive histogram algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrences of each intensity value by adding ones to the corre…
Visual TrackingLearning Compact Binary Codes for Visual Tracking
A key problem in visual tracking is to represent the appearance of an object in a way that is robust to visual changes. To attain this robustness, increasingly complex models are used to capture appearance variations. Ho…
Visual TrackingPart-Based Visual Tracking with Online Latent Structural Learning
Despite many advances made in the area, deformable targets and partial occlusions continue to represent key problems in visual tracking. Structured learning has shown good results when applied to tracking whole targets, …
Structured PredictionVisual TrackingMinimum Uncertainty Gap for Robust Visual Tracking
We propose a novel tracking algorithm that robustly tracks the target by finding the state which minimizes uncertainty of the likelihood at current state. The uncertainty of the likelihood is estimated by obtaining the g…
Visual TrackingA Diffusion Process on Riemannian Manifold for Visual Tracking
Robust visual tracking for long video sequences is a research area that has many important applications. The main challenges include how the target image can be modeled and how this model can be updated. In this paper, w…
Visual TrackingStructBoost: Boosting Methods for Predicting Structured Output Variables
Boosting is a method for learning a single accurate predictor by linearly combining a set of less accurate weak learners. Recently, structured learning has found many applications in computer vision. Inspired by structur…
Image SegmentationMulti-class ClassificationSemantic SegmentationVisual TrackingOn the connections between saliency and tracking
A model connecting visual tracking and saliency has recently been proposed. This model is based on the saliency hypothesis for tracking which postulates that tracking is achieved by the top-down tuning, based on target f…
Visual TrackingFusion with Diffusion for Robust Visual Tracking
A weighted graph is used as an underlying structure of many algorithms like semi-supervised learning and spectral clustering. The edge weights are usually deter-mined by a single similarity measure, but it often hard if …
ClusteringVisual TrackingReal-time face swapping as a tool for understanding infant self-recognition
To study the preference of infants for contingency of movements and familiarity of faces during self-recognition task, we built, as an accurate and instantaneous imitator, a real-time face- swapper for videos. We present…
Face SwappingVisual TrackingMultiple Instance Filtering
We propose a robust filtering approach based on semi-supervised and multiple instance learning (MIL). We assume that the posterior density would be unimodal if not for the effect of outliers that we do not wish to explic…
Multiple Instance LearningVisual Tracking