# KHAIT: K-9 Handler Artificial Intelligence Teaming for Collaborative Sensemaking

arXiv: https://arxiv.org/abs/2503.15524 · HTML: https://mlsanity.com/paper/2503.15524/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 80 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations](https://mlsanity.com/paper/2012.09988/) (2020)
- [DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets](https://mlsanity.com/paper/2201.08812/) (2022)
- [Assessing User Apprehensions About Mixed Reality Artifacts and Applications: The Mixed Reality Concerns (MRC) Questionnaire](https://mlsanity.com/paper/2403.05855/) (2024)
- [Exploring the Design Space of Optical See-through AR Head-Mounted Displays to Support First Responders in the Field](https://mlsanity.com/paper/2403.04660/) (2024)

## Built on by (most cited first, 1 of 1)

- [HydroVision: Predicting Optically Active Parameters in Surface Water Using Computer Vision](https://mlsanity.com/paper/2509.01882/) (2025)

## Abstract (arXiv)

In urban search and rescue (USAR) operations, communication between handlers and specially trained canines is crucial but often complicated by challenging environments and the specific behaviors canines are trained to exhibit when detecting a person. Since a USAR canine often works out of sight of the handler, the handler lacks awareness of the canine's location and situation, known as the 'sensemaking gap.' In this paper, we propose KHAIT, a novel approach to close the sensemaking gap and enhance USAR effectiveness by integrating object detection-based Artificial Intelligence (AI) and Augmented Reality (AR). Equipped with AI-powered cameras, edge computing, and AR headsets, KHAIT enables precise and rapid object detection from a canine's perspective, improving survivor localization. We evaluate this approach in a real-world USAR environment, demonstrating an average survival allocation time decrease of 22%, enhancing the speed and accuracy of operations.