AI Guide Dog: Egocentric Path Prediction on Smartphone
원문 초록
This paper presents AI Guide Dog (AIGD), a lightweight egocentric (first-person) navigation system for visually impaired users, designed for real-time deployment on smartphones. AIGD employs a vision-only multi-label classification approach to predict directional commands, ensuring safe navigation across diverse environments. We introduce a novel technique for goal-based outdoor navigation by integrating GPS signals and high-level directions, while also handling uncertain multi-path predictions for destination-free indoor navigation. As the first navigation assistance system to handle both goal-oriented and exploratory navigation across indoor and outdoor settings, AIGD establishes a new benchmark in blind navigation. We present methods, datasets, evaluations, and deployment insights to encourage further innovations in assistive navigation systems.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
WXImpactBench: A Disruptive Weather Impact Understanding Benchmark for Evaluating Large Language Models
JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models
Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference
Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.