Collision detection based on scale change of image segments for the visually impaired

Sung Ho Chae; Jee Young Sun; Mun Cheon Kang; Byoung Jun Son; Sung-Jea Ko

doi:10.1109/ICCE.2015.7066504

Collision detection based on scale change of image segments for the visually impaired

Sung Ho Chae
, Jee Young Sun
, Mun Cheon Kang
, Byoung Jun Son
, Sung-Jea Ko

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

A variety of portable or wearable navigation systems mounted on smart glasses and smartphones have been developed to assist visually impaired people over the last decade. In these systems, collision detection is one of the key components. Many conventional methods with the monocular vision estimate the collision risk based on the motion information of obstacles in the image by measuring the size change of objects using detected feature points and their corresponding motion vectors. However, the size change is sometimes incorrectly measured due to unreliable feature points and motion vectors. To overcome this problem, we present a motion clustering scheme to remove outliers among both feature points and motion vectors. Experimental results indicate that the proposed collision detection method outperforms the conventional one in terms of detection and false positive rates.

Original language	English
Title of host publication	2015 IEEE International Conference on Consumer Electronics, ICCE 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	511-512
Number of pages	2
ISBN (Print)	9781479975426
DOIs	https://doi.org/10.1109/ICCE.2015.7066504
Publication status	Published - 2015 Mar 23
Event	2015 IEEE International Conference on Consumer Electronics, ICCE 2015 - Las Vegas, United States Duration: 2015 Jan 9 → 2015 Jan 12

Other

Other	2015 IEEE International Conference on Consumer Electronics, ICCE 2015
Country/Territory	United States
City	Las Vegas
Period	15/1/9 → 15/1/12

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering

Access to Document

10.1109/ICCE.2015.7066504

Cite this

Collision detection based on scale change of image segments for the visually impaired. / Chae, Sung Ho; Sun, Jee Young; Kang, Mun Cheon et al.
2015 IEEE International Conference on Consumer Electronics, ICCE 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 511-512 7066504.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chae, SH, Sun, JY, Kang, MC, Son, BJ & Ko, S-J 2015, Collision detection based on scale change of image segments for the visually impaired. in 2015 IEEE International Conference on Consumer Electronics, ICCE 2015., 7066504, Institute of Electrical and Electronics Engineers Inc., pp. 511-512, 2015 IEEE International Conference on Consumer Electronics, ICCE 2015, Las Vegas, United States, 15/1/9. https://doi.org/10.1109/ICCE.2015.7066504

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abstract = "A variety of portable or wearable navigation systems mounted on smart glasses and smartphones have been developed to assist visually impaired people over the last decade. In these systems, collision detection is one of the key components. Many conventional methods with the monocular vision estimate the collision risk based on the motion information of obstacles in the image by measuring the size change of objects using detected feature points and their corresponding motion vectors. However, the size change is sometimes incorrectly measured due to unreliable feature points and motion vectors. To overcome this problem, we present a motion clustering scheme to remove outliers among both feature points and motion vectors. Experimental results indicate that the proposed collision detection method outperforms the conventional one in terms of detection and false positive rates.",

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AU - Chae, Sung Ho

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AB - A variety of portable or wearable navigation systems mounted on smart glasses and smartphones have been developed to assist visually impaired people over the last decade. In these systems, collision detection is one of the key components. Many conventional methods with the monocular vision estimate the collision risk based on the motion information of obstacles in the image by measuring the size change of objects using detected feature points and their corresponding motion vectors. However, the size change is sometimes incorrectly measured due to unreliable feature points and motion vectors. To overcome this problem, we present a motion clustering scheme to remove outliers among both feature points and motion vectors. Experimental results indicate that the proposed collision detection method outperforms the conventional one in terms of detection and false positive rates.

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Collision detection based on scale change of image segments for the visually impaired

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