Visual Object Tracking
150 papers with code • 21 benchmarks • 26 datasets
Visual Object Tracking is an important research topic in computer vision, image understanding and pattern recognition. Given the initial state (centre location and scale) of a target in the first frame of a video sequence, the aim of Visual Object Tracking is to automatically obtain the states of the object in the subsequent video frames.
Benchmarks
These leaderboards are used to track progress in Visual Object Tracking
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Libraries
Use these libraries to find Visual Object Tracking models and implementations
OTB
LaSOT
GOT-10k
TrackingNet
OTB-2015
YouTube-VOS 2018
VOT2018
VOT2016
OTB-2013
VOT2017
Most implemented papers
SiamVGG: Visual Tracking using Deeper Siamese Networks
It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking.
Ocean: Object-aware Anchor-free Tracking
researchmm/TracKit
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ECCV 2020
In this paper, we propose a novel object-aware anchor-free network to address this issue.
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
wangxiao5791509/Single_Object_Tracking_Paper_List
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Tracking using bio-inspired event cameras has drawn more and more attention in recent years.
Staple: 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.
ATOM: Accurate Tracking by Overlap Maximization
visionml/pytracking
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CVPR 2019
We argue that this approach is fundamentally limited since target estimation is a complex task, requiring high-level knowledge about the object.
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.
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
Guanghan/ROLO
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In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking.
Learning Video Object Segmentation from Static Images
omkar13/MaskTrack
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CVPR 2017
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentation.
Robust Estimation of Similarity Transformation for Visual Object Tracking
In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result.
Fast Video Object Segmentation by Reference-Guided Mask Propagation
seoungwugoh/RGMP
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CVPR 2018
We validate our method on four benchmark sets that cover single and multiple object segmentation.
