Papers Visual Tracking
“Visual Tracking” 태그가 달린 논문 581편 · 필터 해제
Efficient Discriminative Nonorthogonal Binary Subspace with its Application to Visual Tracking
One of the crucial problems in visual tracking is how the object is represented. Conventional appearance-based trackers are using increasingly more complex features in order to be robust. However, complex representations…
feature selectionObjectObject TrackingTemplate Matching+1Robust Visual Tracking via Inverse Nonnegative Matrix Factorization
The establishment of robust target appearance model over time is an overriding concern in visual tracking. In this paper, we propose an inverse nonnegative matrix factorization (NMF) method for robust appearance modeling…
Visual TrackingOnline Metric-Weighted Linear Representations for Robust Visual Tracking
In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric le…
Metric LearningObjectObject TrackingVisual TrackingStructural Sparse Tracking
Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only conside…
Visual TrackingReliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches
Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization. In this paper, we propose a new tracking meth…
ClusteringVisual TrackingTowards Force Sensing From Vision: Observing Hand-Object Interactions to Infer Manipulation Forces
We present a novel, non-intrusive approach for estimating contact forces during hand-object interactions relying solely on visual input provided by a single RGB-D camera. We consider a manipulated object with known geom…
Visual TrackingReal-Time Part-Based Visual Tracking via Adaptive Correlation Filters
Robust object tracking is a challenging task in computer vision. To better solve the partial occlusion issue, part-based methods are widely used in visual object trackers. However, due to the complicated online training …
Bayesian InferenceObject TrackingVisual TrackingMultihypothesis Trajectory Analysis for Robust Visual Tracking
The notion of multihypothesis trajectory analysis (MTA) for robust visual tracking is proposed in this work. We employ multiple component trackers using texture, color, and illumination invariant features, respectively. …
Visual TrackingLong-Term Correlation Tracking
In this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-the-view. In this setting…
TranslationVisual TrackingUnsupervised Learning of Visual Representations using Videos
Is strong supervision necessary for learning a good visual representation? Do we really need millions of semantically-labeled images to train a Convolutional Neural Network (CNN)? In this paper, we present a simple yet s…
Surface Normal EstimationTripletVisual TrackingUnderstanding and Diagnosing Visual Tracking Systems
Several benchmark datasets for visual tracking research have been proposed in recent years. Despite their usefulness, whether they are sufficient for understanding and diagnosing the strengths and weaknesses of different…
Visual TrackingDeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking
Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking because they require very long training time and a l…
Visual TrackingOnline Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
We propose an online visual tracking algorithm by learning discriminative saliency map using Convolutional Neural Network (CNN). Given a CNN pre-trained on a large-scale image repository in offline, our algorithm takes o…
Visual TrackingVisual object tracking performance measures revisited
The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments. This makes the cross-paper tra…
ObjectObject TrackingVisual Object TrackingVisual TrackingRobust Visual Tracking via Convolutional Networks
Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However the offline training is time-consuming and the learned generic represent…
Visual TrackingInstance Significance Guided Multiple Instance Boosting for Robust Visual Tracking
Multiple Instance Learning (MIL) recently provides an appealing way to alleviate the drifting problem in visual tracking. Following the tracking-by-detection framework, an online MILBoost approach is developed that seque…
Multiple Instance LearningVisual TrackingTransferring Rich Feature Hierarchies for Robust Visual Tracking
Convolutional neural network (CNN) models have demonstrated great success in various computer vision tasks including image classification and object detection. However, some equally important tasks such as visual trackin…
image-classificationImage ClassificationObjectobject-detection+3Adaptive Objectness for Object Tracking
Object tracking is a long standing problem in vision. While great efforts have been spent to improve tracking performance, a simple yet reliable prior knowledge is left unexploited: the target object in tracking must be …
ObjectObject TrackingVisual Object TrackingVisual TrackingAdaptive Color Attributes for Real-Time Visual Tracking
Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracki…
AttributeImage DescriptionObject RecognitionReal-Time Visual Tracking+1Multi-Cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation
The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated. Since different types of variations such as illumination, occlusion and pose may…
Visual Tracking