Papers Visual Object Tracking
“Visual Object Tracking” 태그가 달린 논문 369편 · 필터 해제
Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking
Object-to-camera motion produces a variety of apparent motion patterns that significantly affect performance of short-term visual trackers. Despite being crucial for designing robust trackers, their influence is poorly e…
AttributeObject TrackingVisual Object TrackingECO: Efficient Convolution Operators for Tracking
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic s…
CPUDiversityVisual Object TrackingDiscriminative Correlation Filter with Channel and Spatial Reliability
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and pr…
CPUVisual Object TrackingOne-Shot Video Object Segmentation
This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation…
Foreground SegmentationObjectSegmentationSemi-Supervised Video Object Segmentation+3Convolutional Regression for Visual Tracking
Recently, discriminatively learned correlation filters (DCF) has drawn much attention in visual object tracking community. The success of DCF is potentially attributed to the fact that a large amount of samples are utili…
Object TrackingregressionVisual Object TrackingVisual TrackingMultiple Object Tracking with Kernelized Correlation Filters in Urban Mixed Traffic
Recently, the Kernelized Correlation Filters tracker (KCF) achieved competitive performance and robustness in visual object tracking. On the other hand, visual trackers are not typically used in multiple object tracking.…
Multiple Object TrackingObjectObject TrackingVisual Object TrackingDiscriminative Scale Space Tracking
Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strate…
Object TrackingVisual Object TrackingModeling and Propagating CNNs in a Tree Structure for Visual Tracking
We present an online visual tracking algorithm by managing multiple target appearance models in a tree structure. The proposed algorithm employs Convolutional Neural Networks (CNNs) to represent target appearances, where…
Object TrackingVisual Object TrackingVisual TrackingBeyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted…
Object TrackingPoint TrackingVisual Object TrackingVisual TrackingSpatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the di…
Binary Classificationobject-detectionObject DetectionObject Tracking+2Learning feed-forward one-shot learners
One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learning scenarios, are ill-suited for one-sh…
Object TrackingOne-Shot LearningVisual Object TrackingInstance-Level Video Segmentation From Object Tracks
We address the problem of segmenting multiple object instances in complex videos. Our method does not require manual pixel-level annotation for training, and relies instead on readily-available object detectors or visual…
ClusteringObjectObject TrackingSegmentation+4Once 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+1Multi-modal Tracking for Object based SLAM
We present an on-line 3D visual object tracking framework for monocular cameras by incorporating spatial knowledge and uncertainty from semantic mapping along with high frequency measurements from visual odometry. Using …
ObjectObject TrackingVisual Object TrackingVisual OdometryFast Integral Image Estimation at 1% measurement rate
We propose a framework called ReFInE to directly obtain integral image estimates from a very small number of spatially multiplexed measurements of the scene without iterative reconstruction of any auxiliary image, and de…
Object TrackingVisual Object TrackingSSD: Single Shot MultiBox Detector
We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and…
LIDAR Semantic SegmentationLow-Light Image EnhancementNode Property PredictionObject+3Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn…
regressionVideo Object TrackingVisual Object TrackingExploring Causal Relationships in Visual Object Tracking
Causal relationships can often be found in visual object tracking between the motions of the camera and that of the tracked object. This object motion may be an effect of the camera motion, e.g. an unsteady handheld came…
ObjectObject TrackingVisual Object 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 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 Tracking