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PoLaRIS Dataset: A Maritime Object Detection and Tracking Dataset in Pohang Canal

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arxiv 2412.06192 v2 pith:A5GUBBN4 submitted 2024-12-09 cs.RO

classification cs.RO
keywords datasetmaritimedetectiontrackingannotationsenvironmentsevaluationshazardous
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Maritime environments often present hazardous situations due to factors such as moving ships or buoys, which become obstacles under the influence of waves. In such challenging conditions, the ability to detect and track potentially hazardous objects is critical for the safe navigation of marine robots. To address the scarcity of comprehensive datasets capturing these dynamic scenarios, we introduce a new multi-modal dataset that includes image and point-wise annotations of maritime hazards. Our dataset provides detailed ground truth for obstacle detection and tracking, including objects as small as 10$\times$10 pixels, which are crucial for maritime safety. To validate the dataset's effectiveness as a reliable benchmark, we conducted evaluations using various methodologies, including \ac{SOTA} techniques for object detection and tracking. These evaluations are expected to contribute to performance improvements, particularly in the complex maritime environment. To the best of our knowledge, this is the first dataset offering multi-modal annotations specifically tailored to maritime environments. Our dataset is available at https://sites.google.com/view/polaris-dataset.

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Cited by 1 Pith paper

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  1. MVTD: A Benchmark Dataset for Maritime Visual Object Tracking

    cs.CV 2025-06 conditional novelty 5.0 of 10

    MVTD is a 182-sequence, 150,000-frame maritime visual tracking benchmark with four object classes, showing that state-of-the-art trackers degrade on maritime scenes and improve after fine-tuning.

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