Fully-Convolutional Siamese Networks for Object Tracking
Luca Bertinetto,Jack Valmadre,João F. Henriques,Andrea Vedaldi,Philip H. S. Torr +4 more
- Vol. 9914, pp 850-865
TLDR
A basic tracking algorithm is equipped with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video and achieves state-of-the-art performance in multiple benchmarks.Abstract:
The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object’s appearance exclusively online, using as sole training data the video itself. Despite the success of these methods, their online-only approach inherently limits the richness of the model they can learn. Recently, several attempts have been made to exploit the expressive power of deep convolutional networks. However, when the object to track is not known beforehand, it is necessary to perform Stochastic Gradient Descent online to adapt the weights of the network, severely compromising the speed of the system. In this paper we equip a basic tracking algorithm with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video. Our tracker operates at frame-rates beyond real-time and, despite its extreme simplicity, achieves state-of-the-art performance in multiple benchmarks.read more
Citations
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Proceedings ArticleDOI
High Performance Visual Tracking with Siamese Region Proposal Network
TL;DR: The Siamese region proposal network (Siamese-RPN) is proposed which is end-to-end trained off-line with large-scale image pairs for visual object tracking and consists of SiAMESe subnetwork for feature extraction and region proposal subnetwork including the classification branch and regression branch.
Proceedings ArticleDOI
ECO: Efficient Convolution Operators for Tracking
TL;DR: This work revisit the core DCF formulation and introduces a factorized convolution operator, which drastically reduces the number of parameters in the model, and a compact generative model of the training sample distribution that significantly reduces memory and time complexity, while providing better diversity of samples.
Posted Content
Exploring Simple Siamese Representation Learning
Xinlei Chen,Kaiming He +1 more
TL;DR: Surprising empirical results are reported that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders.
Proceedings ArticleDOI
SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks
TL;DR: This work proves the core reason Siamese trackers still have accuracy gap comes from the lack of strict translation invariance, and proposes a new model architecture to perform depth-wise and layer-wise aggregations, which not only improves the accuracy but also reduces the model size.
Proceedings ArticleDOI
End-to-End Representation Learning for Correlation Filter Based Tracking
TL;DR: In this paper, the Correlation Filter learner is interpreted as a differentiable layer in a deep neural network, which enables learning deep features that are tightly coupled to the correlation filter.
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