More Like this

1. Generative Multi-View Human Action Recognition

   Wang, L.; Ding, Z.; Tao, Z.; Liu, Y.; Fu, Y. (January 2019, International Conference on Computer Vision)
2. MAP: Multi-Human-Value Alignment Palette

   Wang, Xinran; Le, Qi; Ahmed, Ammar; Diao, Enmao; Zhou, Yi; Baracaldo, Nathalie; Ding, Jie; Anwar, Ali (April 2025, International Conference on Learning Representations)
3. Collaborative multi-robot multi-human teams in search and rescue.

   Williams, R. (April 2020, Proceedings of the International ISCRAM Conference)

   Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that fields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy. 

   more » « less
4. Optimal Real-Time Human Attention Allocation and Scheduling in a Multi-human and Multi-robot Collaborative System

   https://doi.org/10.1109/CCTA60707.2024.10666642  

   Yao, Ningshi; Wang, Renke (August 2024, IEEE)
5. ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

   https://doi.org/10.1371/journal.pone.0255259  

   Li, Zhenlong; Huang, Xiao; Hu, Tao; Ning, Huan; Ye, Xinyue; Huang, Binghu; Li, Xiaoming (August 2021, PLOS ONE)

   Yang, Chaowei (Ed.)

   In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics. 

   more » « less