Learning Features with Differentiable Closed-Form Solver for Tracking

by   Linyu Zheng, et al.

We present a novel and easy-to-implement training framework for visual tracking. Our approach mainly focuses on learning feature embeddings in an end-to-end way, which can generalize well to the trackers based on online discriminatively trained ridge regression model. This goal is efficiently achieved by taking advantage of the following two important theories. 1) Ridge regression problem has closed-form solution and is implicit differentiation under the optimality condition. Therefore, its solver can be embedded as a layer with efficient forward and backward processes in training deep convolutional neural networks. 2) Woodbury identity can be utilized to ensure efficient solution of ridge regression problem when the high-dimensional feature embeddings are employed. Moreover, in order to address the extreme foreground-background class imbalance during training, we modify the origin shrinkage loss and then employ it as the loss function for efficient and effective training. It is worth mentioning that the above core parts of our proposed training framework are easy to be implemented with several lines of code under the current popular deep learning frameworks, thus our approach is easy to be followed. Extensive experiments on six public benchmarks, OTB2015, NFS, TrackingNet, GOT10k, VOT2018, and VOT2019, show that the proposed tracker achieves state-of-the-art performance, while running at over 30 FPS. Code will be made available.


page 3

page 8


DomainSiam: Domain-Aware Siamese Network for Visual Object Tracking

Visual object tracking is a fundamental task in the field of computer vi...

Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination

Visual tracking can be easily disturbed by similar surrounding objects. ...

Convolutional Regression for Visual Tracking

Recently, discriminatively learned correlation filters (DCF) has drawn m...

Boosting Ridge Regression for High Dimensional Data Classification

Ridge regression is a well established regression estimator which can co...

Deep Regression Ensembles

We introduce a methodology for designing and training deep neural networ...

Hallucinated Adversarial Learning for Robust Visual Tracking

Humans can easily learn new concepts from just a single exemplar, mainly...

Meta-learning with differentiable closed-form solvers

Adapting deep networks to new concepts from few examples is extremely ch...

Please sign up or login with your details

Forgot password? Click here to reset