# Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking arXiv: https://arxiv.org/abs/2104.03114 ยท HTML: https://mlsanity.com/paper/2104.03114/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 76 references that are in the map. - First posted on arXiv 7 Apr 2021. ## Builds on (most cited first, 30 of 32) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018) - [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016) - [Deep Learning without Poor Local Minima](https://mlsanity.com/paper/1605.07110/) (2016) - [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020) - [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018) - [Siam R-CNN: Visual Tracking by Re-Detection](https://mlsanity.com/paper/1911.12836/) (2019) - [The Shattered Gradients Problem: If resnets are the answer, then what is the question?](https://mlsanity.com/paper/1702.08591/) (2017) - [Know Your Surroundings: Exploiting Scene Information for Object Tracking](https://mlsanity.com/paper/2003.11014/) (2020) - [End-to-end representation learning for Correlation Filter based tracking](https://mlsanity.com/paper/1704.06036/) (2017) - [Unsupervised Deep Tracking](https://mlsanity.com/paper/1904.01828/) (2019) - [D3S -- A Discriminative Single Shot Segmentation Tracker](https://mlsanity.com/paper/1911.08862/) (2019) - [Learning Spatially Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1608.05571/) (2016) - [Learning Background-Aware Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1703.04590/) (2017) - [Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1803.08679/) (2018) - [AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization](https://mlsanity.com/paper/2003.12949/) (2020) - [State-Aware Tracker for Real-Time Video Object Segmentation](https://mlsanity.com/paper/2003.00482/) (2020) - [Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking](https://mlsanity.com/paper/1908.02231/) (2019) - [Discriminative Scale Space Tracking](https://mlsanity.com/paper/1609.06141/) (2016) - [Discriminative Correlation Filter with Channel and Spatial Reliability](https://mlsanity.com/paper/1611.08461/) (2016) - [Target-Aware Deep Tracking](https://mlsanity.com/paper/1904.01772/) (2019) - [GradNet: Gradient-Guided Network for Visual Object Tracking](https://mlsanity.com/paper/1909.06800/) (2019) - [CREST: Convolutional Residual Learning for Visual Tracking](https://mlsanity.com/paper/1708.00225/) (2017) - [Fast Template Matching and Update for Video Object Tracking and Segmentation](https://mlsanity.com/paper/2004.07538/) (2020) - [Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking](https://mlsanity.com/paper/1609.06118/) (2016) - [Kernel Cross-Correlator](https://mlsanity.com/paper/1709.05936/) (2017) - [Learning Binary Residual Representations for Domain-specific Video Streaming](https://mlsanity.com/paper/1712.05087/) (2017) - [Training-Set Distillation for Real-Time UAV Object Tracking](https://mlsanity.com/paper/2003.05326/) (2020) - [Keyfilter-Aware Real-Time UAV Object Tracking](https://mlsanity.com/paper/2003.05218/) (2020) - [Augmented Memory for Correlation Filters in Real-Time UAV Tracking](https://mlsanity.com/paper/1909.10989/) (2019) ## Built on by (most cited first, 9 of 9) - [Tracking Small and Fast Moving Objects: A Benchmark](https://mlsanity.com/paper/2209.04284/) (2022) - [Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking](https://mlsanity.com/paper/2308.11450/) (2023) - [Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking](https://mlsanity.com/paper/2308.10262/) (2023) - [Tracking Transforming Objects: A Benchmark](https://mlsanity.com/paper/2404.18143/) (2024) - [Equivalence of Correlation Filter and Convolution Filter in Visual Tracking](https://mlsanity.com/paper/2105.00158/) (2021) - [Tracking Reflected Objects: A Benchmark](https://mlsanity.com/paper/2407.05235/) (2024) - [Learning an Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking](https://mlsanity.com/paper/2412.20002/) (2024) - [TrackingMiM: Efficient Mamba-in-Mamba Serialization for Real-time UAV Object Tracking](https://mlsanity.com/paper/2507.01535/) (2025) - [STATrack: A Target-Aware Fully Spiking Neural Network for Efficient UAV Tracking](https://mlsanity.com/paper/2603.27493/) (2026) ## Abstract (arXiv) Unmanned aerial vehicle (UAV)-based tracking is attracting increasing attention and developing rapidly in applications such as agriculture, aviation, navigation, transportation and public security. Recently, discriminative correlation filters (DCF)-based trackers have stood out in UAV tracking community for their high efficiency and appealing robustness on a single CPU. However, due to limited onboard computation resources and other challenges the efficiency and accuracy of existing DCF-based approaches is still not satisfying. In this paper, we explore using segmentation by the GrabCut to improve the wildly adopted discriminative scale estimation in DCF-based trackers, which, as a mater of fact, greatly impacts the precision and accuracy of the trackers since accumulated scale error degrades the appearance model as online updating goes on. Meanwhile, inspired by residue representation, we exploit the residue nature inherent to videos and propose residue-aware correlation filters that show better convergence properties in filter learning. Extensive experiments are conducted on four UAV benchmarks, namely, UAV123@10fps, DTB70, UAVDT and Vistrone2018 (VisDrone2018-test-dev). The results show that our method achieves state-of-the-art performance.