# A Real-Time Region Tracking Algorithm Tailored to Endoscopic Video with Open-Source Implementation

arXiv: https://arxiv.org/abs/2203.08858 · HTML: https://mlsanity.com/paper/2203.08858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Fast Optical Flow using Dense Inverse Search](https://mlsanity.com/paper/1603.03590/) (2016)
- [Discriminative Correlation Filter with Channel and Spatial Reliability](https://mlsanity.com/paper/1611.08461/) (2016)
- [Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation](https://mlsanity.com/paper/2005.05050/) (2020)
- [Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures](https://mlsanity.com/paper/2006.14321/) (2020)

## Abstract (arXiv)

With a video data source, such as multispectral video acquired during administration of fluorescent tracers, extraction of time-resolved data typically requires the compensation of motion. While this can be done manually, which is arduous, or using off-the-shelf object tracking software, which often yields unsatisfactory performance, we present an algorithm which is simple and performant. Most importantly, we provide an open-source implementation, with an easy-to-use interface for researchers not inclined to write their own code, as well as Python modules that can be used programmatically.