# Recurrent Filter Learning for Visual Tracking

arXiv: https://arxiv.org/abs/1708.03874 · HTML: https://mlsanity.com/paper/1708.03874/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2017.

## Built on by (most cited first, 4 of 4)

- [MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking](https://mlsanity.com/paper/2107.10433/) (2021)
- [Global Instance Tracking: Locating Target More Like Humans](https://mlsanity.com/paper/2202.13073/) (2022)
- [Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking](https://mlsanity.com/paper/2112.14016/) (2021)
- [Beyond Traditional Single Object Tracking: A Survey](https://mlsanity.com/paper/2405.10439/) (2024)

## Abstract (arXiv)

Recently using convolutional neural networks (CNNs) has gained popularity in visual tracking, due to its robust feature representation of images. Recent methods perform online tracking by fine-tuning a pre-trained CNN model to the specific target object using stochastic gradient descent (SGD) back-propagation, which is usually time-consuming. In this paper, we propose a recurrent filter generation methods for visual tracking. We directly feed the target's image patch to a recurrent neural network (RNN) to estimate an object-specific filter for tracking. As the video sequence is a spatiotemporal data, we extend the matrix multiplications of the fully-connected layers of the RNN to a convolution operation on feature maps, which preserves the target's spatial structure and also is memory-efficient. The tracked object in the subsequent frames will be fed into the RNN to adapt the generated filters to appearance variations of the target. Note that once the off-line training process of our network is finished, there is no need to fine-tune the network for specific objects, which makes our approach more efficient than methods that use iterative fine-tuning to online learn the target. Extensive experiments conducted on widely used benchmarks, OTB and VOT, demonstrate encouraging results compared to other recent methods.