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
Most implemented papers
Learning regression and verification networks for long-term visual tracking
xiaobai1217/MBMD
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Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if present or conducting image-wide re-detection if absent.
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.
Fast Online Object Tracking and Segmentation: A Unifying Approach
foolwood/SiamMask
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CVPR 2019
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
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.
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
HyeonseobNam/py-MDNet
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CVPR 2016
Our algorithm pretrains a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation.
Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network
These regularities are hard to label for training supervised machine learning algorithms; consequently, algorithms need to learn these regularities from the real world in an unsupervised way.
Tracking using Numerous Anchor points
In this paper, an online adaptive model-free tracker is proposed to track single objects in video sequences to deal with real-world tracking challenges like low-resolution, object deformation, occlusion and motion blur.
Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping
The tracking scheme is coherently integrated into a perceptual grouping framework in which the visual tracking problem is tackled by identifying a subset of these line segments and connecting them sequentially to form a closed boundary with the largest saliency and a certain similarity to the previous one.
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online.
Learning Discriminative Model Prediction for Tracking
visionml/pytracking
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ICCV 2019
The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking.
