# Robust Estimation of Similarity Transformation for Visual Object Tracking

arXiv: https://arxiv.org/abs/1712.05231 · HTML: https://mlsanity.com/paper/1712.05231/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2017.

## Built on by (most cited first, 10 of 10)

- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking](https://mlsanity.com/paper/2101.08466/) (2021)
- [Active Learning for Deep Visual Tracking](https://mlsanity.com/paper/2110.13259/) (2021)
- [Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition](https://mlsanity.com/paper/2109.09166/) (2021)
- [Homography Decomposition Networks for Planar Object Tracking](https://mlsanity.com/paper/2112.07909/) (2021)
- [Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking](https://mlsanity.com/paper/2202.10881/) (2022)
- [VGGT-SLAM++](https://mlsanity.com/paper/2604.06830/) (2026)
- [NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object Tracking](https://mlsanity.com/paper/2410.20421/) (2024)
- [Convolutional Unscented Kalman Filter for Multi-Object Tracking with Outliers](https://mlsanity.com/paper/2406.01380/) (2024)
- [Warped Convolutional Networks: Bridge Homography to sl(3) algebra by Group Convolution](https://mlsanity.com/paper/2206.11657/) (2022)

## Abstract (arXiv)

Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements. In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result. Specifically, we employ an efficient phase correlation scheme to deal with both scale and rotation changes simultaneously in log-polar coordinates. Moreover, a variant of correlation filter is used to predict the translational motion individually. Our experimental results demonstrate that the proposed tracker achieves very promising prediction performance compared with the state-of-the-art visual object tracking methods while still retaining the advantages of high efficiency and simplicity in conventional correlation filter-based tracking methods.