# Discriminative Correlation Filter with Channel and Spatial Reliability

arXiv: https://arxiv.org/abs/1611.08461 · HTML: https://mlsanity.com/paper/1611.08461/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2016.

## Built on by (most cited first, 30 of 45)

- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking](https://mlsanity.com/paper/2103.11681/) (2021)
- [Transforming Model Prediction for Tracking](https://mlsanity.com/paper/2203.11192/) (2022)
- [Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook](https://mlsanity.com/paper/2112.02838/) (2021)
- [Robust Visual Tracking by Segmentation](https://mlsanity.com/paper/2203.11191/) (2022)
- [Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking](https://mlsanity.com/paper/2101.08466/) (2021)
- [Gravity-Aware Monocular 3D Human-Object Reconstruction](https://mlsanity.com/paper/2108.08844/) (2021)
- [Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition](https://mlsanity.com/paper/2109.01305/) (2021)
- [Global Instance Tracking: Locating Target More Like Humans](https://mlsanity.com/paper/2202.13073/) (2022)
- [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)
- [SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery](https://mlsanity.com/paper/2302.03022/) (2023)
- [Learning of Global Objective for Network Flow in Multi-Object Tracking](https://mlsanity.com/paper/2203.16210/) (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)
- [Object Tracking and Geo-localization from Street Images](https://mlsanity.com/paper/2107.06257/) (2021)
- [Dynamic Modeling of Hand-Object Interactions via Tactile Sensing](https://mlsanity.com/paper/2109.04378/) (2021)
- [Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation](https://mlsanity.com/paper/2504.19347/) (2025)
- [Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks](https://mlsanity.com/paper/2103.02969/) (2021)
- [SiamCorners: Siamese Corner Networks for Visual Tracking](https://mlsanity.com/paper/2104.07303/) (2021)
- [Video-rate multispectral imaging in laparoscopic surgery: First-in-human application](https://mlsanity.com/paper/2105.13901/) (2021)
- [Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label](https://mlsanity.com/paper/2106.08073/) (2021)
- [UHP-SOT: An Unsupervised High-Performance Single Object Tracker](https://mlsanity.com/paper/2110.01812/) (2021)
- [SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications](https://mlsanity.com/paper/2507.03578/) (2025)
- [Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator](https://mlsanity.com/paper/2008.13193/) (2020)
- [Distilling Channels for Efficient Deep Tracking](https://mlsanity.com/paper/2409.11785/) (2024)
- [A Discriminative Single-Shot Segmentation Network for Visual Object Tracking](https://mlsanity.com/paper/2112.11846/) (2021)
- [Equivalence of Correlation Filter and Convolution Filter in Visual Tracking](https://mlsanity.com/paper/2105.00158/) (2021)
- [Unsupervised Lightweight Single Object Tracking with UHP-SOT++](https://mlsanity.com/paper/2111.07548/) (2021)
- [Nerve Block Target Localization and Needle Guidance for Autonomous Robotic Ultrasound Guided Regional Anesthesia](https://mlsanity.com/paper/2308.03717/) (2023)
- [Long Term Object Detection and Tracking in Collaborative Learning Environments](https://mlsanity.com/paper/2106.07556/) (2021)

## Abstract (arXiv)

Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard features, HoGs and Colornames, the novel CSR-DCF method -- DCF with Channel and Spatial Reliability -- achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs in real-time on a CPU.