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ForeSightGuide: An Anticipatory Framework toward Accurate and Low-Redundancy Guidance for the Visually Impaired — arXiv:2608.18993

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18993
License (as archived): CC0-1.0
Archived: 2026-10-18T14:06:32.008Z
SHA-256 of the archived text: c55e6082d03469d781f77b52b18117bec5213c723a06c7344afda0e21093ca1e
Stable URL of this page: https://metraipolis.com/documento/dc_bdef364bda47

How to cite

ForeSightGuide: An Anticipatory Framework toward Accurate and Low-Redundancy Guidance for the Visually Impaired — arXiv:2608.18993 — arXiv (en), https://arxiv.org/abs/2608.18993 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bdef364bda47 (Metraipolis archive dc_bdef364bda47, sha256 c55e6082d03469d781f77b52b18117bec5213c723a06c7344afda0e21093ca1e)

BibTeX

@misc{dcbdef364bda47,
  title = {ForeSightGuide: An Anticipatory Framework toward Accurate and Low-Redundancy Guidance for the Visually Impaired — arXiv:2608.18993},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18993},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bdef364bda47; sha256 c55e6082d03469d781f77b52b18117bec5213c723a06c7344afda0e21093ca1e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18993. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Electronic travel aids are pivotal for the independent mobility of the visually impaired. While Vision-Language Models (VLMs) offer rich environmental understanding, they often suffer from excessive false positives in dynamic scenarios, leading to cognitive overload. To address this, we present ForeSightGuide, an anticipatory assistive guidance framework that couples semantic scene understanding with predictive hazard assessment. Unlike reactive systems, ForeSightGuide leverages the reasoning capabilities of VLMs to anticipate obstacle motion, effectively filtering out non-threatening objects to provide concise, actionable guidance. To validate our approach, we introduce a novel dataset captured in complex, dynamic real-world traffic scenes, designed to benchmark predictive capabilities. Extensive experiments on both public benchmarks and our proposed dataset demonstrate that ForeSightGuide achieves state-of-the-art performance. Notably, it significantly mitigates information overload by reducing redundant alerts to 0.299 per guidance output while maintaining a low missed-hazard rate of 0.112, proving its efficacy for safe walking assistance.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-20 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.18993 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T14:00:22.589Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).