# GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors

arXiv: https://arxiv.org/abs/2402.14136 · HTML: https://mlsanity.com/paper/2402.14136/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Domain Adaptation for Visual Applications: A Comprehensive Survey](https://mlsanity.com/paper/1702.05374/) (2017)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection](https://mlsanity.com/paper/2012.00914/) (2020)
- [MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark](https://mlsanity.com/paper/2111.15157/) (2021)
- [Multimodal sensor fusion in the latent representation space](https://mlsanity.com/paper/2208.02183/) (2022)
- [Radar Enlighten the Dark: Enhancing Low-Visibility Perception for Automated Vehicles with Camera-Radar Fusion](https://mlsanity.com/paper/2305.17318/) (2023)
- [A Multimodal Sensor Fusion Framework Robust to Missing Modalities for Person Recognition](https://mlsanity.com/paper/2210.10972/) (2022)

## Built on by (most cited first, 3 of 3)

- [MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT](https://mlsanity.com/paper/2411.12126/) (2024)
- [FlexLoc: Conditional Neural Networks for Zero-Shot Sensor Perspective Invariance in Object Localization with Distributed Multimodal Sensors](https://mlsanity.com/paper/2406.06796/) (2024)
- [ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise and Compute Resources](https://mlsanity.com/paper/2502.07862/) (2025)

## Abstract (arXiv)

Constantly locating moving objects, i.e., geospatial tracking, is essential for autonomous building infrastructure. Accurate and robust geospatial tracking often leverages multimodal sensor fusion algorithms, which require large datasets with time-aligned, synchronized data from various sensor types. However, such datasets are not readily available. Hence, we propose GDTM, a nine-hour dataset for multimodal object tracking with distributed multimodal sensors and reconfigurable sensor node placements. Our dataset enables the exploration of several research problems, such as optimizing architectures for processing multimodal data, and investigating models' robustness to adverse sensing conditions and sensor placement variances. A GitHub repository containing the code, sample data, and checkpoints of this work is available at https://github.com/nesl/GDTM.