Object Detection in a Maritime Environment: Performance Evaluation of Background Subtraction Methods

TLDR

The study benchmarks 23 background subtraction algorithms on visible and near‑infrared maritime videos from the Singapore Maritime dataset. The authors evaluate each algorithm on the dataset, measuring standard performance metrics across both spectral ranges. The evaluation reveals that conventional metrics are inadequate for maritime vision, highlights four key challenges—spurious water dynamics, wakes, ghost effects, and multiple detections—and shows that all 23 methods suffer from poor recall and precision, underscoring the need for maritime‑specific background subtraction techniques.

Abstract

This paper provides a benchmark of the performance of 23 classical and state-of-the-art background subtraction (BS) algorithms on visible range and near infrared range videos in the Singapore Maritime dataset. Importantly, our study indicates the limitations of the conventional performance evaluation criteria for maritime vision and proposes new performance evaluation criteria that is better suited to this problem. This paper provides insight into the specific challenges of BS in maritime vision. We identify four open challenges that plague BS methods in maritime scenario. These include spurious dynamics of water, wakes, ghost effect, and multiple detections. Poor recall and extremely poor precision of all the 23 methods, which have been otherwise successful for other challenging BS situations, allude to the need for new BS methods custom designed for maritime vision.