Real-Time Visual Tracking
10 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Real-Time Visual Tracking
Drunkard's Dataset
Most implemented papers
Deeper and Wider Siamese Networks for Real-Time Visual Tracking
researchmm/SiamDW
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CVPR 2019
Siamese networks have drawn great attention in visual tracking because of their balanced accuracy and speed.
Fast Online Object Tracking and Segmentation: A Unifying Approach
foolwood/SiamMask
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CVPR 2019
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
Fully Convolutional Online Tracking
MCG-NJU/FCOT
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To tackle this issue, we present the fully convolutional online tracking framework, coined as FCOT, and focus on enabling online learning for both classification and regression branches by using a target filter based tracking paradigm.
SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World
Finally, we use a pre-rendered sparse viewpoint model to create a joint posterior probability for the object pose.
Visual Pursuit Control based on Gaussian Processes with Switched Motion Trajectories
This paper considers a scenario of pursuing a moving target that may switch behaviors due to external factors in a dynamic environment by motion estimation using visual sensors.
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements
Chinmayrane16/ReconNet-PyTorch
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CVPR 2016
The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image.
Real-time visual tracking by deep reinforced decision making
In this paper, we introduce a novel real-time visual tracking algorithm based on a template selection strategy constructed by deep reinforcement learning methods.
Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network
ecart18/SATA
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Our tracker achieves leading performance in OTB2013, OTB2015, VOT2015, VOT2016 and LaSOT, and operates at a real-time speed of 26 FPS, which indicates our method is effective and practical.
DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression
By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity.
BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models
Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching.
