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
Libraries
Use these libraries to find Visual Tracking models and implementationsMost implemented papers
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
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
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
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
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
Siamese networks have drawn great attention in visual tracking because of their balanced accuracy and speed.
ATOM: Accurate Tracking by Overlap Maximization
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
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
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.