# Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking

arXiv: https://arxiv.org/abs/1803.08679 · HTML: https://mlsanity.com/paper/1803.08679/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 30 of 63)

- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking](https://mlsanity.com/paper/2103.11681/) (2021)
- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search](https://mlsanity.com/paper/2104.14545/) (2021)
- [HiFT: Hierarchical Feature Transformer for Aerial Tracking](https://mlsanity.com/paper/2108.00202/) (2021)
- [TCTrack: Temporal Contexts for Aerial Tracking](https://mlsanity.com/paper/2203.01885/) (2022)
- [Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook](https://mlsanity.com/paper/2112.02838/) (2021)
- [Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking](https://mlsanity.com/paper/2101.08466/) (2021)
- [All-Day Object Tracking for Unmanned Aerial Vehicle](https://mlsanity.com/paper/2101.08446/) (2021)
- [All in One: Exploring Unified Vision-Language Tracking with Multi-Modal Alignment](https://mlsanity.com/paper/2307.03373/) (2023)
- [WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking](https://mlsanity.com/paper/2201.07425/) (2022)
- [Towards Real-World Visual Tracking with Temporal Contexts](https://mlsanity.com/paper/2308.10330/) (2023)
- [Visual Object Tracking in First Person Vision](https://mlsanity.com/paper/2209.13502/) (2022)
- [Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking](https://mlsanity.com/paper/2503.06625/) (2025)
- [Is First Person Vision Challenging for Object Tracking?](https://mlsanity.com/paper/2108.13665/) (2021)
- [Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark](https://mlsanity.com/paper/2111.12960/) (2021)
- [ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking](https://mlsanity.com/paper/2106.02495/) (2021)
- [Siamese Transformer Pyramid Networks for Real-Time UAV Tracking](https://mlsanity.com/paper/2110.08822/) (2021)
- [SiamMask: A Framework for Fast Online Object Tracking and Segmentation](https://mlsanity.com/paper/2207.02088/) (2022)
- [Tracking Small and Fast Moving Objects: A Benchmark](https://mlsanity.com/paper/2209.04284/) (2022)
- [Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking](https://mlsanity.com/paper/2104.03114/) (2021)
- [Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking](https://mlsanity.com/paper/2308.11450/) (2023)
- [Progressive Representation Learning for Real-Time UAV Tracking](https://mlsanity.com/paper/2409.16652/) (2024)
- [Rank-Based Filter Pruning for Real-Time UAV Tracking](https://mlsanity.com/paper/2207.01768/) (2022)
- [Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label](https://mlsanity.com/paper/2106.08073/) (2021)
- [Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking](https://mlsanity.com/paper/2108.00187/) (2021)
- [UHP-SOT: An Unsupervised High-Performance Single Object Tracker](https://mlsanity.com/paper/2110.01812/) (2021)
- [Local Perception-Aware Transformer for Aerial Tracking](https://mlsanity.com/paper/2208.00662/) (2022)
- [Privacy-sensitive Objects Pixelation for Live Video Streaming](https://mlsanity.com/paper/2101.00604/) (2021)

## Abstract (arXiv)

Discriminative Correlation Filters (DCF) are efficient in visual tracking but suffer from unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested to resolve this issue by enforcing spatial penalty on DCF coefficients, which, inevitably, improves the tracking performance at the price of increasing complexity. To tackle online updating, SRDCF formulates its model on multiple training images, further adding difficulties in improving efficiency. In this work, by introducing temporal regularization to SRDCF with single sample, we present our spatial-temporal regularized correlation filters (STRCF). Motivated by online Passive-Agressive (PA) algorithm, we introduce the temporal regularization to SRDCF with single sample, thus resulting in our spatial-temporal regularized correlation filters (STRCF). The STRCF formulation can not only serve as a reasonable approximation to SRDCF with multiple training samples, but also provide a more robust appearance model than SRDCF in the case of large appearance variations. Besides, it can be efficiently solved via the alternating direction method of multipliers (ADMM). By incorporating both temporal and spatial regularization, our STRCF can handle boundary effects without much loss in efficiency and achieve superior performance over SRDCF in terms of accuracy and speed. Experiments are conducted on three benchmark datasets: OTB-2015, Temple-Color, and VOT-2016. Compared with SRDCF, STRCF with hand-crafted features provides a 5 times speedup and achieves a gain of 5.4% and 3.6% AUC score on OTB-2015 and Temple-Color, respectively. Moreover, STRCF combined with CNN features also performs favorably against state-of-the-art CNN-based trackers and achieves an AUC score of 68.3% on OTB-2015.