# Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

arXiv: https://arxiv.org/abs/1807.11348 · HTML: https://mlsanity.com/paper/1807.11348/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 25 of 25)

- [Transformer Tracking with Cyclic Shifting Window Attention](https://mlsanity.com/paper/2205.03806/) (2022)
- [Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2108.11510/) (2021)
- [FEAR: Fast, Efficient, Accurate and Robust Visual Tracker](https://mlsanity.com/paper/2112.07957/) (2021)
- [WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking](https://mlsanity.com/paper/2201.07425/) (2022)
- [Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking](https://mlsanity.com/paper/2201.08673/) (2022)
- [Learning to Filter: Siamese Relation Network for Robust Tracking](https://mlsanity.com/paper/2104.00829/) (2021)
- [An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs](https://mlsanity.com/paper/2105.01342/) (2021)
- [TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object Tracking](https://mlsanity.com/paper/2405.05004/) (2024)
- [Learning Localization-aware Target Confidence for Siamese Visual Tracking](https://mlsanity.com/paper/2204.14093/) (2022)
- [Temporal Aggregation for Adaptive RGBT Tracking](https://mlsanity.com/paper/2201.08949/) (2022)
- [A Survey for Deep RGBT Tracking](https://mlsanity.com/paper/2201.09296/) (2022)
- [Cognitive Disentanglement for Referring Multi-Object Tracking](https://mlsanity.com/paper/2503.11496/) (2025)
- [Underwater Camouflaged Object Tracking Meets Vision-Language SAM2](https://mlsanity.com/paper/2409.16902/) (2024)
- [GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences](https://mlsanity.com/paper/2207.07629/) (2022)
- [In Defense of Clip-based Video Relation Detection](https://mlsanity.com/paper/2307.08984/) (2023)
- [Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking](https://mlsanity.com/paper/2111.11625/) (2021)
- [RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification](https://mlsanity.com/paper/2503.09033/) (2025)
- [COST: Contrastive One-Stage Transformer for Vision-Language Small Object Tracking](https://mlsanity.com/paper/2504.01321/) (2025)
- [Spatio-Temporal Matching for Siamese Visual Tracking](https://mlsanity.com/paper/2105.02408/) (2021)
- [New Performance Measures for Object Tracking under Complex Environments](https://mlsanity.com/paper/2111.07145/) (2021)
- [Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking](https://mlsanity.com/paper/2112.14016/) (2021)
- [Sparse Regularized Correlation Filter for UAV Object Tracking with adaptive Contextual Learning and Keyfilter Selection](https://mlsanity.com/paper/2205.03627/) (2022)
- [Unsupervised Green Object Tracker (GOT) without Offline Pre-training](https://mlsanity.com/paper/2309.09078/) (2023)
- [Staged Depthwise Correlation and Feature Fusion for Siamese Object Tracking](https://mlsanity.com/paper/2310.09747/) (2023)
- [GAN-HA: A generative adversarial network with a novel heterogeneous dual-discriminator network and a new attention-based fusion strategy for infrared and visible image fusion](https://mlsanity.com/paper/2404.15992/) (2024)

## Abstract (arXiv)

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches.