Papers Visual Tracking
“Visual Tracking” 태그가 달린 논문 581편 · 필터 해제
Visual Tracking via Spatially Aligned Correlation Filters Network
Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background. This assumption however yields undesired boundary effects and restricts asp…
Visual TrackingDeep Reinforcement Learning with Iterative Shift for Visual Tracking
Visual tracking is confronted by the dilemma to locate a target both}accurately and efficiently, and make decisions online whether and how to adapt the appearance model or even restart tracking. In this paper, we propose…
Deep Reinforcement LearningMotion EstimationObjectObject Tracking+4Real-Time MDNet
We present a fast and accurate visual tracking algorithm based on the multi-domain convolutional neural network (MDNet). The proposed approach accelerates feature extraction procedure and learns more discriminative model…
Visual TrackingDistractor-aware Siamese Networks for Visual Object Tracking
Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. However, features used in most Siamese tracking approaches can only discriminate foreground…
Incremental LearningObjectObject TrackingVideo Object Tracking+2Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications
Compute and memory demands of state-of-the-art deep learning methods are still a shortcoming that must be addressed to make them useful at IoT end-nodes. In particular, recent results depict a hopeful prospect for image …
BenchmarkingDeep LearningEdge-computingVisual TrackingLearning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suf…
Benchmarkingfeature selectionObject TrackingVideo Object Tracking+1SafeDrive: Enhancing Lane Appearance for Autonomous and Assisted Driving Under Limited Visibility
Autonomous detection of lane markers improves road safety, and purely visual tracking is desirable for widespread vehicle compatibility and reducing sensor intrusion, cost, and energy consumption. However, visual approac…
Autonomous DrivingLane DetectionVisual TrackingWho did What at Where and When: Simultaneous Multi-Person Tracking and Activity Recognition
We present a bootstrapping framework to simultaneously improve multi-person tracking and activity recognition at individual, interaction and social group activity levels. The inference consists of identifying trajectorie…
Activity RecognitionVisual TrackingStochastic Channel Decorrelation Network and Its Application to Visual Tracking
Deep convolutional neural networks (CNNs) have dominated many computer vision domains because of their great power to extract good features automatically. However, many deep CNNs-based computer vison tasks suffer from la…
DiversityVisual TrackingFast Dynamic Convolutional Neural Networks for Visual Tracking
Most of the existing tracking methods based on CNN(convolutional neural networks) are too slow for real-time application despite the excellent tracking precision compared with the traditional ones. In this paper, a fast …
Visual TrackingState-aware Anti-drift Robust Correlation Tracking
Correlation filter (CF) based trackers have aroused increasing attentions in visual tracking field due to the superior performance on several datasets while maintaining high running speed. For each frame, an ideal filter…
Visual TrackingTracking Emerges by Colorizing Videos
We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model that learns to colorize gray-scale video…
ColorizationOptical Flow EstimationSkeleton Based Action RecognitionVisual TrackingLearning to Update for Object Tracking with Recurrent Meta-learner
Model update lies at the heart of object tracking. Generally, model update is formulated as an online learning problem where a target model is learned over the online training set. Our key innovation is to \emph{formulat…
GPUMeta-LearningObject TrackingVisual Object Tracking+1Fast Kernelized Correlation Filters without Boundary Effect
In recent years, correlation filter based trackers (CF trackers) have attracted much attention from the vision community because of their top performance in both localization accuracy and efficiency. The society of visua…
Visual TrackingCorrelation Tracking via Robust Region Proposals
Recently, correlation filter-based trackers have received extensive attention due to their simplicity and superior speed. However, such trackers perform poorly when the target undergoes occlusion, viewpoint change or oth…
Region ProposalVisual TrackingRobust Structured Multi-task Multi-view Sparse Tracking
Sparse representation is a viable solution to visual tracking. In this paper, we propose a structured multi-task multi-view tracking (SMTMVT) method, which exploits the sparse appearance model in the particle filter fram…
Visual TrackingLearning to track on-the-fly using a particle filter with annealed- weighted QPSO modeled after a singular Dirac delta potential
This paper proposes an evolutionary Particle Filter with a memory guided proposal step size update and an improved, fully-connected Quantum-behaved Particle Swarm Optimization (QPSO) resampling scheme for visual tracking…
Visual TrackingSynthetic data generation for end-to-end thermal infrared tracking
The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutiona…
Image-to-Image TranslationSynthetic Data GenerationTranslationVisual TrackingMulti-Cue Correlation Filters for Robust Visual Tracking
In recent years, many tracking algorithms achieve impressive performance via fusing multiple types of features, however, most of them fail to fully explore the context among the adopted multiple features and the strength…
CPUVisual TrackingLearning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
Offline training for object tracking has recently shown great potentials in balancing tracking accuracy and speed. However, it is still difficult to adapt an offline trained model to a target tracked online. This work pr…
Object TrackingRepresentation LearningVisual Tracking