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
Latest papers with no code
Exploring Dynamic Transformer for Efficient Object Tracking
For instance, DyTrack obtains 64. 9% AUC on LaSOT with a speed of 256 fps.
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning
To evaluate the effectiveness of our general framework OneTracker, which is consisted of Foundation Tracker and Prompt Tracker, we conduct extensive experiments on 6 popular tracking tasks across 11 benchmarks and our OneTracker outperforms other models and achieves state-of-the-art performance.
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks
Moreover, it can be seamlessly integrated with other visual trackers as a plug-and-play module without requiring any parameter adjustments.
ACTrack: Adding Spatio-Temporal Condition for Visual Object Tracking
Efficiently modeling spatio-temporal relations of objects is a key challenge in visual object tracking (VOT).
Reading Relevant Feature from Global Representation Memory for Visual Object Tracking
Therefore, using all features in the template and memory can lead to redundancy and impair tracking performance.
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements
In this work, we incorporate concepts from signal sparsity into variational regularization for motion estimation.
X Modality Assisting RGBT Object Tracking
Learning robust multi-modal feature representations is critical for boosting tracking performance.
Tracking Skiers from the Top to the Bottom
To enable the study, the largest and most annotated dataset for computer vision in skiing, SkiTB, is introduced.
Distractor-aware Event-based Tracking
We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency.
RTrack: Accelerating Convergence for Visual Object Tracking via Pseudo-Boxes Exploration
Single object tracking (SOT) heavily relies on the representation of the target object as a bounding box.
