Visual Tracking
168 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
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Libraries
Use these libraries to find Visual Tracking models and implementations
Kinetics
DAVIS
OTB
LaSOT
TrackingNet
OTB-2015
OTB-2013
TNL2K
Kubric
OxUva
Latest papers with no code
Exploiting Image-Related Inductive Biases in Single-Branch Visual Tracking
Moreover, the effectiveness of discriminative trackers remains constrained due to the adoption of the dual-branch pipeline.
Staged Depthwise Correlation and Feature Fusion for Siamese Object Tracking
In this work, we propose a novel staged depthwise correlation and feature fusion network, named DCFFNet, to further optimize the feature extraction for visual tracking.
BASE: Probably a Better Approach to Multi-Object Tracking
The field of visual object tracking is dominated by methods that combine simple tracking algorithms and ad hoc schemes.
Efficient Training for Visual Tracking with Deformable Transformer
Recent Transformer-based visual tracking models have showcased superior performance.
Towards Efficient Training with Negative Samples in Visual Tracking
This study introduces a more efficient training strategy to mitigate overfitting and reduce computational requirements.
Robust Visual Tracking by Motion Analyzing
In this paper, we propose a new algorithm that addresses this limitation by analyzing the motion pattern using the inherent tensor structure.
HHTrack: Hyperspectral Object Tracking Using Hybrid Attention
Hyperspectral imagery provides abundant spectral information beyond the visible RGB bands, offering rich discriminative details about objects in a scene.
Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking
The Bridge Module incorporates the high-level information of deep features into the shallow large-resolution features.
Low-complexity Multidimensional DCT Approximations
In general, the suggested methods showed competitive performance at a considerably lower computational cost.
SiamTHN: Siamese Target Highlight Network for Visual Tracking
The majority of siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and hence challenging to focus on the target region.
