2021/09/26 by Bhavesh Kumar, Bhavesh Shri Kumar, Kumar, Bhavesh
Computer Science · Engineering · Neuroscience · #Artificial Intelligence (cs.AI) #C.3 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Smart Parking Systems Research #Tactile and Sensory Interactions #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2109.13857
4 pages, 4 figures
arxiv created 2021/09/26 · openalex publication_date 2021/09/26 · arxiv updated 2021/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Blind and visually challenged face multiple issues with navigating the world independently. Some of these challenges include finding the shortest path to a destination and detecting obstacles from a distance. To tackle this issue, this paper proposes ViT Cane, which leverages a vision transformer model in order to detect obstacles in real-time. Our entire system consists of a Pi Camera Module v2, Raspberry Pi 4B with 8GB Ram and 4 motors. Based on tactile input using the 4 motors, the obstacle detection model is highly efficient in helping visually impaired navigate unknown terrain and is designed to be easily reproduced. The paper discusses the utility of a Visual Transformer model in comparison to other CNN based models for this specific application. Through rigorous testing, the proposed obstacle detection model has achieved higher performance on the Common Object in Context (COCO) data set than its CNN counterpart. Comprehensive field tests were conducted to verify the effectiveness of our system for holistic indoor understanding and obstacle avoidance.