Papers Visual Object Tracking
“Visual Object Tracking” 태그가 달린 논문 369편 · 필터 해제
Siamese Keypoint Prediction Network for Visual Object Tracking
Visual object tracking aims to estimate the location of an arbitrary target in a video sequence given its initial bounding box. By utilizing offline feature learning, the siamese paradigm has recently been the leading fr…
ObjectObject TrackingPredictionVisual Object TrackingD3S - A Discriminative Single Shot Segmentation Tracker
Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of transformation models, which reduces their l…
ObjectObject TrackingSegmentationSemantic Segmentation+4One-Shot Adversarial Attacks on Visual Tracking With Dual Attention
Almost all adversarial attacks in computer vision are aimed at pre-known object categories, which could be offline trained for generating perturbations. But as for visual object tracking, the tracked target categories ar…
Adversarial AttackObjectObject TrackingVisual Object Tracking+1AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking
Siamese approaches have achieved promising performance in visual object tracking recently. The key to the success of Siamese trackers is to learn appearance-invariant feature embedding functions via pair-wise offline tra…
BenchmarkingObject TrackingOne-Shot LearningVisual Object TrackingRobust Visual Object Tracking with Two-Stream Residual Convolutional Networks
The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised training data in offline mode. However,…
Object TrackingVisual Object TrackingVisual TrackingVocal Bursts Valence PredictionTSDM: Tracking by SiamRPN++ with a Depth-refiner and a Mask-generator
In a generic object tracking, depth (D) information provides informative cues for foreground-background separation and target bounding box regression. However, so far, few trackers have used depth information to play the…
Object TrackingVisual Object TrackingHow to Train Your Energy-Based Model for Regression
Energy-based models (EBMs) have become increasingly popular within computer vision in recent years. While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, …
object-detectionObject DetectionregressionVisual Object Tracking+1Deformable Siamese Attention Networks for Visual Object Tracking
Siamese-based trackers have achieved excellent performance on visual object tracking. However, the target template is not updated online, and the features of the target template and search image are computed independentl…
ObjectObject TrackingVisual Object 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 PredictionDMV: Visual Object Tracking via Part-level Dense Memory and Voting-based Retrieval
We propose a novel memory-based tracker via part-level dense memory and voting-based retrieval, called DMV. Since deep learning techniques have been introduced to the tracking field, Siamese trackers have attracted many …
Object TrackingRetrievalTemplate MatchingVisual Object TrackingSiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking
Recently spiking neural networks (SNNs), the third-generation of neural networks has shown remarkable capabilities of energy-efficient computing, which is a promising alternative for deep neural networks (DNNs) with high…
image-classificationImage ClassificationObject TrackingVisual Object TrackingTraining-Set Distillation for Real-Time UAV Object Tracking
Correlation filter (CF) has recently exhibited promising performance in visual object tracking for unmanned aerial vehicle (UAV). Such online learning method heavily depends on the quality of the training-set, yet compli…
CPUObjectObject TrackingVisual Object TrackingCRVOS: Clue Refining Network for Video Object Segmentation
The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances. However, most of them have complex intermediate networks wh…
DecoderObjectSegmentationSemantic Segmentation+4PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i.e., the confidence set for a given inpu…
Generalization BoundsLearning TheoryObject Trackingreinforcement-learning+3Rethinking Convolutional Features in Correlation Filter Based Tracking
Both accuracy and efficiency are of significant importance to the task of visual object tracking. In recent years, as the surge of deep learning, Deep Convolutional NeuralNetwork (DCNN) becomes a very popular choice amon…
feature selectionObject TrackingVisual Object TrackingKernel 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+8An Accelerated Correlation Filter Tracker
Recent visual object tracking methods have witnessed a continuous improvement in the state-of-the-art with the development of efficient discriminative correlation filters (DCF) and robust deep neural network features. De…
BenchmarkingObject TrackingVisual Object TrackingSiamese 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+1Siam R-CNN: Visual Tracking by Re-Detection
We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. We combine this with a novel tracklet-based dynamic programmi…
Objectobject-detectionObject DetectionObject Tracking+3