Visual Tracking
193 papers with code • 9 benchmarks • 27 datasets
Visual Tracking is an essential and actively researched problem in the field of computer vision with various real-world applications such as robotic services, smart surveillance systems, autonomous driving, and human-computer interaction. It refers to the automatic estimation of the trajectory of an arbitrary target object, usually specified by a bounding box in the first frame, as it moves around in subsequent video frames.
Source: Learning Reinforced Attentional Representation for End-to-End Visual Tracking
Benchmarks
These leaderboards are used to track progress in Visual Tracking
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Libraries
Use these libraries to find Visual Tracking models and implementations
Kinetics
DAVIS
OTB
LaSOT
TrackingNet
OTB-2015
OTB-2013
Kubric
TNL2K
TAP-Vid
Most implemented papers
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
STVIR/pysot
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CVPR 2019
Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size.
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
danielgordon10/re3-tensorflow
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Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time.
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
MegviiDetection/video_analyst
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Following these guidelines, we design our Fully Convolutional Siamese tracker++ (SiamFC++) by introducing both classification and target state estimation branch(G1), classification score without ambiguity(G2), tracking without prior knowledge(G3), and estimation quality score(G4).
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.
High Performance Visual Tracking With Siamese Region Proposal Network
foolwood/DaSiamRPN
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CVPR 2018
Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on multiple benchmarks.
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.
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.
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.
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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CVPR 2024
Tracking using bio-inspired event cameras has drawn more and more attention in recent years.
Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline
event-ahu/felt_sot_benchmark
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Current event-/frame-event based trackers undergo evaluation on short-term tracking datasets, however, the tracking of real-world scenarios involves long-term tracking, and the performance of existing tracking algorithms in these scenarios remains unclear.
