Visual Tracking
169 papers with code • 9 benchmarks • 26 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Visual Tracking models and implementations
Kinetics
DAVIS
OTB
LaSOT
TrackingNet
OTB-2015
OTB-2013
TNL2K
Kubric
OxUva
Most implemented papers
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
STVIR/pysot
•
•
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
natdebru/OpenCV-Video-Label
•
•
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
•
•
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
•
•
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
•
•
CVPR 2019
Siamese networks have drawn great attention in visual tracking because of their balanced accuracy and speed.
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
•
•
Tracking using bio-inspired event cameras has drawn more and more attention in recent years.
Kernel Cross-Correlator
Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking.
Real-Time MDNet
IlchaeJung/RT-MDNet
•
•
ECCV 2018
We present a fast and accurate visual tracking algorithm based on the multi-domain convolutional neural network (MDNet).
