Papers Visual Tracking
“Visual Tracking” 태그가 달린 논문 581편 · 필터 해제
Once for All: a Two-flow Convolutional Neural Network for Visual Tracking
One of the main challenges of visual object tracking comes from the arbitrary appearance of objects. Most existing algorithms try to resolve this problem as an object-specific task, i.e., the model is trained to regenera…
AllObjectObject TrackingVisual Object Tracking+1Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle
Visual representation is crucial for a visual tracking method's performances. Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors. These descriptors were dev…
Visual TrackingAudio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion
Speaker diarization consists of assigning speech signals to people engaged in a dialogue. An audio-visual spatiotemporal diarization model is proposed. The model is well suited for challenging scenarios that consist of s…
Clusteringspeaker-diarizationSpeaker DiarizationVisual TrackingGeometric Hypergraph Learning for Visual Tracking
Graph based representation is widely used in visual tracking field by finding correct correspondences between target parts in consecutive frames. However, most graph based trackers consider pairwise geometric relations b…
Visual TrackingA Universal Update-pacing Framework For Visual Tracking
This paper proposes a novel framework to alleviate the model drift problem in visual tracking, which is based on paced updates and trajectory selection. Given a base tracker, an ensemble of trackers is generated, in whic…
Visual TrackingVisual Tracking via Reliable Memories
In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks. First, we design a Discrete Fourie…
ClusteringVisual TrackingReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements
The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutiona…
Real-Time Visual TrackingVisual TrackingLearning Support Correlation Filters for Visual Tracking
Sampling and budgeting training examples are two essential factors in tracking algorithms based on support vector machines (SVMs) as a trade-off between accuracy and efficiency. Recently, the circulant matrix formed by d…
Visual TrackingDeep Tracking: Visual Tracking Using Deep Convolutional Networks
In this paper, we study a discriminatively trained deep convolutional network for the task of visual tracking. Our tracker utilizes both motion and appearance features that are extracted from a pre-trained dual stream de…
Visual TrackingSOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking
A simple yet effective object descriptor for visual tracking is proposed in this paper. We first decompose the bounding box of a target object into multiple patches, which are described by color and gradient histograms. …
ObjectVisual TrackingMulti-Kernel Correlation Filter for Visual Tracking
Correlation filter based trackers are ranked top in terms of performances. Nevertheless, they only employ a single kernel at a time. In this paper, we will derive a multi-kernel correlation filter (MKCF) based tracker wh…
Visual TrackingHierarchical Convolutional Features for Visual Tracking
Visual object tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we exploit features extracted fr…
Object RecognitionObject TrackingVisual Object TrackingVisual TrackingVisual Tracking With Fully Convolutional Networks
We propose a new approach for general object tracking with fully convolutional neural network. Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the pro…
Object TrackingVisual TrackingMultiple Feature Fusion via Weighted Entropy for Visual Tracking
It is desirable to combine multiple feature descriptors to improve the visual tracking performance because different features can provide complementary information to describe objects of interest. However, how to effecti…
ObjectObject TrackingVisual Object TrackingVisual TrackingLocal Subspace Collaborative Tracking
Subspace models have been widely used for appearance based object tracking. Most existing subspace based trackers employ a linear subspace to represent object appearances, which are not accurate enough to model large var…
ObjectObject TrackingVisual TrackingLinearization to Nonlinear Learning for Visual Tracking
Due to unavoidable appearance variations caused by occlusion, deformation, and other factors, classifiers for visual tracking are nonlinear as a necessity. Building on the theory of globally linear approximations to nonl…
DescriptiveDictionary LearningVisual TrackingFirst Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks
In this paper, we propose and study a novel visual object tracking approach based on convolutional networks and recurrent networks. The proposed approach is distinct from the existing approaches to visual object tracking…
ObjectObject TrackingVisual Object TrackingVisual TrackingLearning Multi-Domain Convolutional Neural Networks for Visual Tracking
We propose a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network (CNN). Our algorithm pretrains a CNN using a large set of videos with tracking ground…
Binary ClassificationGeneral ClassificationVisual TrackingVisual Tracking via Nonnegative Regularization Multiple Locality Coding
This paper presents a novel object tracking method based on approximated Locality-constrained Linear Coding (LLC). Rather than using a non-negativity constraint on encoding coefficients to guarantee these elements nonneg…
Object TrackingVisual TrackingRelaxed Multiple-Instance SVM with Application to Object Discovery
Multiple-instance learning (MIL) has served as an important tool for a wide range of vision applications, for instance, image classification, object detection, and visual tracking. In this paper, we propose a novel metho…
General Classificationimage-classificationImage ClassificationMultiple Instance Learning+5