In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos

TL;DR

This paper demonstrates that combining a Kalman filtering tracker with efficient detectors like PP-YOLO enables real-time, accurate polyp detection in colonoscopy videos, outperforming existing methods on multiple datasets.

Contribution

The paper introduces a Kalman filtering tracker that, when combined with PP-YOLO, achieves state-of-the-art real-time polyp detection performance.

Findings

Achieves SOTA detection metrics on CVC-ClinicDB dataset.

Runs at 30 frames per second, enabling real-time application.

Outperforms previous methods on Hyper-Kvasir subset.

Abstract

Real-time and robust automatic detection of polyps from colonoscopy videos are essential tasks to help improve the performance of doctors during this exam. The current focus of the field is on the development of accurate but inefficient detectors that will not enable a real-time application. We advocate that the field should instead focus on the development of simple and efficient detectors that an be combined with effective trackers to allow the implementation of real-time polyp detectors. In this paper, we propose a Kalman filtering tracker that can work together with powerful, but efficient detectors, enabling the implementation of real-time polyp detectors. In particular, we show that the combination of our Kalman filtering with the detector PP-YOLO shows state-of-the-art (SOTA) detection accuracy and real-time processing. More specifically, our approach has SOTA results on the CVC-ClinicDB dataset, with a recall of 0.740, precision of 0.869, $F_1$ score of 0.799, an average precision (AP) of 0.837, and can run in real time (i.e., 30 frames per second). We also evaluate our method on a subset of the Hyper-Kvasir annotated by our clinical collaborators, resulting in SOTA results, with a recall of 0.956, precision of 0.875, $F_1$ score of 0.914, AP of 0.952, and can run in real time.