# Sparse Regularized Correlation Filter for UAV Object Tracking with adaptive Contextual Learning and Keyfilter Selection

arXiv: https://arxiv.org/abs/2205.03627 · HTML: https://mlsanity.com/paper/2205.03627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 16 of 16)

- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [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)
- [Staple: Complementary Learners for Real-Time Tracking](https://mlsanity.com/paper/1512.01355/) (2015)
- [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)
- [Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking](https://mlsanity.com/paper/1807.11348/) (2018)
- [Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments](https://mlsanity.com/paper/1904.02319/) (2019)
- [Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking](https://mlsanity.com/paper/1609.06118/) (2016)
- [Tracking Holistic Object Representations](https://mlsanity.com/paper/1907.12920/) (2019)
- [Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking](https://mlsanity.com/paper/1707.02309/) (2017)
- [Correlation Filters with Limited Boundaries](https://mlsanity.com/paper/1403.7876/) (2014)
- [DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression](https://mlsanity.com/paper/2008.03912/) (2020)

## Abstract (arXiv)

Recently, correlation filter has been widely applied in unmanned aerial vehicle (UAV) tracking due to its high frame rates, robustness and low calculation resources. However, it is fragile because of two inherent defects, i.e, boundary effect and filter corruption. Some methods by enlarging the search area can mitigate the boundary effect, yet introducing the undesired background distractors. Another approaches can alleviate the temporal degeneration of learned filters by introducing the temporal regularizer, which depends on the assumption that the filers between consecutive frames should be coherent. In fact, sometimes the filers at the (t-1)th frame is vulnerable to heavy occlusion from backgrounds, which causes that the assumption does not hold. To handle them, in this work, we propose a novel ℓ1 regularization correlation filter with adaptive contextual learning and keyfilter selection for UAV tracking. Firstly, we adaptively detect the positions of effective contextual distractors by the aid of the distribution of local maximum values on the response map of current frame which is generated by using the previous correlation filter model. Next, we eliminate inconsistent labels for the tracked target by removing one on each distractor and develop a new score scheme for each distractor. Then, we can select the keyfilter from the filters pool by finding the maximal similarity between the target at the current frame and the target template corresponding to each filter in the filters pool. Finally, quantitative and qualitative experiments on three authoritative UAV datasets show that the proposed method is superior to the state-of-the-art tracking methods based on correlation filter framework.