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Meanwhile, convolutional features are extracted to provide a more comprehensive representation of the object.
We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking.
To the best of our knowledge, this is the first paper to propose an online human pose tracking framework in a top-down fashion.
SOTA for Pose Tracking on PoseTrack2017
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.
#2 best model for Visual Object Tracking on VOT2016
Siamese networks have drawn great attention in visual tracking because of their balanced accuracy and speed.
SOTA for Visual Object Tracking on VOT2017
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.
#2 best model for Visual Object Tracking on VOT2017/18
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.
#4 best model for Visual Object Tracking on VOT2017/18
It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations.
Recently, Siamese network based trackers have received tremendous interest for their fast tracking speed and high performance.
#6 best model for Visual Object Tracking on VOT2017/18