# Large Margin Object Tracking with Circulant Feature Maps

arXiv: https://arxiv.org/abs/1703.05020 · HTML: https://mlsanity.com/paper/1703.05020/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2017.

## Built on by (most cited first, 9 of 9)

- [Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook](https://mlsanity.com/paper/2112.02838/) (2021)
- [Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking](https://mlsanity.com/paper/2308.12549/) (2023)
- [Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label](https://mlsanity.com/paper/2106.08073/) (2021)
- [Single Object Tracking Research: A Survey](https://mlsanity.com/paper/2204.11410/) (2022)
- [MFST: Multi-Features Siamese Tracker](https://mlsanity.com/paper/2103.00810/) (2021)
- [Multiple Convolutional Features in Siamese Networks for Object Tracking](https://mlsanity.com/paper/2103.01222/) (2021)
- [Explicitly Modeling the Discriminability for Instance-Aware Visual Object Tracking](https://mlsanity.com/paper/2110.15030/) (2021)
- [Detecting Object Tracking Failure via Sequential Hypothesis Testing](https://mlsanity.com/paper/2602.12983/) (2026)
- [End-to-End Self-Supervised RGB-T Tracking without Modality Misleading](https://mlsanity.com/paper/2609.37162/) (2026)

## Abstract (arXiv)

Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently. Nonetheless, the time-consuming candidate sampling and complex optimization limit their real-time applications. In this paper, we propose a novel large margin object tracking method which absorbs the strong discriminative ability from structured output SVM and speeds up by the correlation filter algorithm significantly. Secondly, a multimodal target detection technique is proposed to improve the target localization precision and prevent model drift introduced by similar objects or background noise. Thirdly, we exploit the feedback from high-confidence tracking results to avoid the model corruption problem. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks (CNNs) based features to validate the strong compatibility of the algorithm. The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per second. The source code and experimental results will be made publicly available.