用吊车激光雷达检测跟踪室内人员，解决视角差异与数据不足问题
Person Detection and Tracking from an Overhead Crane LiDAR
- 基于吊车视角激光雷达，构建专用3D人体标注数据集
- 5米内检测AP达0.84，1米内提升至0.97，VoxelNeXt表现最佳
- 开源数据集与代码，适合工业场景人机感知研究
本文研究在工业室内环境中，通过安装在吊车上的激光雷达实现人员检测与跟踪。吊车视角带来显著领域偏移，且缺乏合适的公开训练数据。为此，我们构建了一个特定场地的吊车激光雷达数据集，包含3D人体边界框标注，并采用统一训练与评估协议适配多个候选3D检测器。进一步结合AB3DMOT和SimpleTrack实现轻量级检测跟踪，以维持人员身份连续性。检测性能采用距离分段评估，量化传感系统的实际工作范围。最佳检测配置在5.0米水平半径内平均精度（AP）达0.84，1.0米内提升至0.97；其中VoxelNeXt与SECOND作为骨干网络表现最稳定。结果有助于弥合标准驾驶数据集与吊车视角感知之间的领域差距。同时报告延迟数据，验证实时可行性。最后，我们将数据集与代码开源至GitHub，支持后续研究。
原文摘要 · Abstract (English)
This paper investigates person detection and tracking in an industrial indoor workspace using a LiDAR mounted on an overhead crane. The overhead viewpoint introduces a strong domain shift from common vehicle-centric LiDAR benchmarks, and limited availability of suitable public training data. Henceforth, we curate a site-specific overhead LiDAR dataset with 3D human bounding-box annotations and adapt selected candidate 3D detectors under a unified training and evaluation protocol. We further integrate lightweight tracking-by-detection using AB3DMOT and SimpleTrack to maintain person identities over time. Detection performance is reported with distance-sliced evaluation to quantify the practical operating envelope of the sensing setup. The best adapted detector configurations achieve average precision (AP) up to 0.84 within a 5.0 m horizontal radius, increasing to 0.97 at 1.0 m, with VoxelNeXt and SECOND emerging as the most reliable backbones across this range. The acquired results contribute in bridging the domain gap between standard driving datasets and overhead sensing for person detection and tracking. We also report latency measurements, highlighting practical real-time feasibility. Finally, we release our dataset and implementations in GitHub to support further research
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