A Real-Time Region Tracking Algorithm Tailored to Endoscopic Video with Open-Source Implementation
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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.
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.
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References · 5
- 2016EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videoscited by 367
- 2016Fast Optical Flow using Dense Inverse Searchcited by 71
- 2016Discriminative Correlation Filter with Channel and Spatial Reliabilitycited by 45
- 2020Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisationcited by 8
- 2020Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signaturescited by 1
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