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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68484
Volume:
10 |
10 |
Oct 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
computer science engineering
sri shakthi institute of engineering and technology
computer science engineering
sri shakthi institute of engineering and technology
computer science engineering
sri shakthi institute of engineering and technology
computer science engineering
sri shakthi institute of engineering and technology
computer science engineering
sri shakthi institute of engineering and technology
computer science engineering
sri shakthi institute of engineering and technology
NavSight is an AI-assisted spatial awareness, object identification, and conversational navigation system designed to empower visually impaired individuals in navigating complex indoor and outdoor environments independently. The system integrates computer vision object detection, ambient audio processing, spatio-temporal memory mapping, multi-language speech interaction (English and Tamil), and proactive collision detection. A lightweight web-accessible architecture powered by Node.js, Web Speech API, spatial detection algorithms, and localized audio feedback supports real-time performance. An operational audit reports 100% passing test modules across navigation, object detection adapters, and conversational context tracking. NavSight bridges spatial memory gaps by providing source-grounded voice guidance, spatial tracking, and emergency obstacle alerts while maintaining minimal cognitive overhead for users.
KEYWORDS
NavSight presents an effective, accessible AI assistance paradigm designed specifically for visually impaired users. By combining object detection, egocentric spatial memory, multilingual voice interfaces, and low-latency safety alerting, the system addresses critical independence barriers in daily mobility. Experimental validation demonstrates high reliability in object tracking and user query processing. The transparent architecture ensures immediate safety overrides, maintaining user confidence during independent navigation.
1. [1] Microsoft, "Seeing AI: Intelligent camera app for the blind community," Official Documentation, 2024. https://www.microsoft.com/en-us/ai/seeing-ai 2. [2] J. Redmon et al., "You Only Look Once: Unified, Real-Time Object Detection," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-788. 3. [3] S. Wang et al., "Egocentric Spatial Memory Mapping in Assistive Robotics," IEEE Transactions on Human-Machine Systems, vol. 51 , no. 4, pp. 312-324, 2021 . 4. [4] W3C, "Web Speech API Specification," W3C Community Group Report, 2023. https://www.w3.org/TR/speech-api/ 5. [5] TensorFlow.js Contributors, "Client-Side Real-Time Machine Learning in Web Browsers," JS Foundation Documentation, 2025. 6. [6] NavSight Project Repository, "Spatial Memory and Navigation Assistive Architecture," System Design Documentation & Test Logs, Oct. 2026.
Publication Date
10/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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