TR2024-117
MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception
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- "MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception", European Conference on Computer Vision (ECCV), September 2024.BibTeX TR2024-117 PDF Data
- @inproceedings{Rahman2024sep,
- author = {Rahman, Mahbub and Yataka, Ryoma and Kato, Sorachi and Wang, Pu and Li, Peizhao and Cardace, Adriano and Boufounos, Petros T.}},
- title = {MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception},
- booktitle = {European Conference on Computer Vision (ECCV)},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-117}
- }
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- "MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception", European Conference on Computer Vision (ECCV), September 2024.
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MERL Contacts:
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Research Areas:
Communications, Computational Sensing, Machine Learning, Signal Processing
Abstract:
Compared with an extensive list of automotive radar datasets that support autonomous driving, indoor radar datasets are scarce at a smaller scale in the format of low-resolution radar point clouds and usu- ally under an open-space single-room setting. In this paper, we scale up indoor radar data collection using multi-view high-resolution radar heatmap in a multi-day, multi-room, and multi-subject setting, with an emphasis on the diversity of environment and subjects. Referred to as the millimeter-wave multi-view radar (MMVR) dataset, it consists of 345K multi-view radar frames collected from 25 human subjects over 6 different rooms, 446K annotated bounding boxes/segmentation instances, and 7.59 million annotated keypoints to support three major perception tasks of object detection, pose estimation, and instance segmentation, respectively. For each task, we report performance benchmarks under two protocols: a single subject in an open space and multiple subjects in several cluttered rooms with two data splits: random split and cross-environment split over 395 1-min data segments. We anticipate that MMVR facilitates indoor radar perception development for indoor vehicle (robot/humanoid) navigation, building energy management, and elderly care for better efficiency, user experience, and safety. The MMVR dataset is available at https://doi.org/10.5281/zenodo.12611978.
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Related Publication
- @article{Rahman2024jun,
- author = {Rahman, Mahbub and Yataka, Ryoma and Kato, Sorachi and Wang, Pu and Li, Peizhao and Cardace, Adriano and Boufounos, Petros T.}},
- title = {MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception},
- journal = {arXiv},
- year = 2024,
- month = jun,
- url = {https://arxiv.org/abs/2406.10708}
- }