# Towards Robust Visual Tracking for Unmanned Aerial Vehicle with Tri-Attentional Correlation Filters

arXiv: https://arxiv.org/abs/2008.00528 · HTML: https://mlsanity.com/paper/2008.00528/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2020.

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

- [Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking](https://mlsanity.com/paper/2104.03114/) (2021)
- [Predictive Visual Tracking: A New Benchmark and Baseline Approach](https://mlsanity.com/paper/2103.04508/) (2021)
- [Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition](https://mlsanity.com/paper/2209.09194/) (2022)
- [Equivalence of Correlation Filter and Convolution Filter in Visual Tracking](https://mlsanity.com/paper/2105.00158/) (2021)

## Abstract (arXiv)

Object tracking has been broadly applied in unmanned aerial vehicle (UAV) tasks in recent years. However, existing algorithms still face difficulties such as partial occlusion, clutter background, and other challenging visual factors. Inspired by the cutting-edge attention mechanisms, a novel object tracking framework is proposed to leverage multi-level visual attention. Three primary attention, i.e., contextual attention, dimensional attention, and spatiotemporal attention, are integrated into the training and detection stages of correlation filter-based tracking pipeline. Therefore, the proposed tracker is equipped with robust discriminative power against challenging factors while maintaining high operational efficiency in UAV scenarios. Quantitative and qualitative experiments on two well-known benchmarks with 173 challenging UAV video sequences demonstrate the effectiveness of the proposed framework. The proposed tracking algorithm favorably outperforms 12 state-of-the-art methods, yielding 4.8% relative gain in UAVDT and 8.2% relative gain in UAV123@10fps against the baseline tracker while operating at the speed of ~ 28 frames per second.