Person Detection and Tracking from an Overhead Crane LiDAR

2026-03-05

AI Summary

This work addresses the significant domain shift between overhead crane-mounted LiDARs—operating under a top-down perspective—and conventional vehicle-mounted LiDAR systems, as well as the lack of publicly available datasets tailored to industrial indoor crane environments. To bridge this gap, the authors present the first LiDAR dataset specifically designed for overhead crane scenarios, featuring 3D human bounding box annotations. Leveraging this dataset, they systematically evaluate and adapt state-of-the-art 3D detectors, including VoxelNeXt and SECOND, and integrate them with tracking-by-detection frameworks such as AB3DMOT and SimpleTrack. A distance-stratified performance analysis demonstrates high detection accuracy, achieving an average precision (AP) of 0.84 within a 5.0-meter horizontal radius and further improving to 0.97 within 1.0 meter, thereby confirming the feasibility of real-time deployment. The dataset and code are publicly released.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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

Problem

• person detection
• person tracking
• overhead LiDAR
• domain shift
• industrial indoor workspace

Innovation

• overhead LiDAR
• person detection and tracking
• domain adaptation
• 3D object detection
• industrial safety