# Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation

arXiv: https://arxiv.org/abs/1710.02039 · HTML: https://mlsanity.com/paper/1710.02039/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2017.

## Built on by (most cited first, 5 of 5)

- [TCTrack: Temporal Contexts for Aerial Tracking](https://mlsanity.com/paper/2203.01885/) (2022)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking](https://mlsanity.com/paper/2106.02495/) (2021)
- [Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label](https://mlsanity.com/paper/2106.08073/) (2021)
- [Benchmarking Deep Trackers on Aerial Videos](https://mlsanity.com/paper/2103.12924/) (2021)

## Abstract (arXiv)

The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance. Although many correlation filter (CF)-based trackers have also been suggested for scale adaptive tracking, few studies have been given to handle the aspect ratio variation for CF trackers. In this paper, we make the first attempt to address this issue by introducing a family of 1D boundary CFs to localize the left, right, top, and bottom boundaries in videos. This allows us cope with the aspect ratio variation flexibly during tracking. Specifically, we present a novel tracking model to integrate 1D Boundary and 2D Center CFs (IBCCF) where boundary and center filters are enforced by a near-orthogonality regularization term. To optimize our IBCCF model, we develop an alternating direction method of multipliers. Experiments on several datasets show that IBCCF can effectively handle aspect ratio variation, and achieves state-of-the-art performance in terms of accuracy and robustness.