Optical flow based obstacle avoidance for the visually impaired

Liyanage, D. K.; Perera, M.U.S.

doi:10.1109/beiac.2012.6226068

Cited by 18 publications

(6 citation statements)

References 17 publications

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“…Some approaches have been used for autonomous robot navigation Systems. In researches as [10] the optical flow is used to determine the direction and the speed of the robot for the next step, and in [11] is presented a project aimed to explore and evaluate the potential optical flow estimation based 978-1-4799-7436-8/15/$31.00 ©2015 IEEE techniques has in guiding a visually impaired person to avoid obstacles using auditory and tactile feedback.…”

Section: Related Workmentioning

confidence: 99%

Decision making for obstacle avoidance in autonomous mobile robots by time to contact and optical flow

Sánchez-García

Ríos-Figueroa

Marı́n-Hernández

et al. 2015

2015 International Conference on Electronics, Communications and Computers (CONIELECOMP)

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Vision is a vital cue for human navigation, and it has become in a powerful tool for robot navigation. Computer vision is useful for recognition of positional relationship and relative motion between themselves and objects in the environment. So, we address the problem of navigation for mobile robot in indoor environment. To this task, we tackle the problem using monocular vision, i.e., without other kind of sensors. Insects and some animals use tools as optical flow estimations and time to contact, to their navigation [1]. So, in this paper, we propose a method for obstacle avoidance, without constantly calculating the optical flow field, only it is calculated when the robot is close to colliding with an obstacle, and so, it uses the flow field divergence to decide which direction will should be taken. Physical experiments using a real robot have been conducted in unknown environments.

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Section: Related Workmentioning

confidence: 99%

Decision making for obstacle avoidance in autonomous mobile robots by time to contact and optical flow

Sánchez-García

Ríos-Figueroa

Marı́n-Hernández

et al. 2015

2015 International Conference on Electronics, Communications and Computers (CONIELECOMP)

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“…Of great interest is the Kalman filter approach to predict camera motion about half a second into the future which was developed further by Molton and Brady [22]. Recently, Liyanage & Perera [17] proposed an optical-flow based obstacle avoidance system for visually impaired people, though the test scenario was a simulated environment that neglected the self-motion of a human.…”

Section: Related Workmentioning

confidence: 99%

“…We compared our method to the widely used counting method which is a method used to estimate the focus of expansion. The counting method was presented by Negahdaripour & Horn [24] and used for example by Souhila et al [34], Liyanage & Perera [17], and McCarthy & Barnes [20]. The counting method is based on the fact that a majority of the vectors point away from the FOE.…”

Section: Estimating Heading Directionmentioning

confidence: 99%

Monocular obstacle avoidance for blind people using probabilistic focus of expansion estimation

Stabinger

Rodríguez-Sánchez

Piater

2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Visually impaired people have a much higher chance of head injuries in daily life because of obstacles that cannot be reliably detected using conventional aids. We present part of a solution to this problem, using only one head mounted camera and optical flow techniques. As part of the system, a novel method to estimate the focus of expansion is presented, which also provides a metric for the quality of the estimate. The final result is a real time capable software system, which can detect obstacles at eye level.

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“…The TTC is defined as the time required for an object in the real world to reach the camera, given the relative speed remains constant. The method in [3] computes motion vectors at each pixel and obtains the focus of expansion (FOE) which indicates an estimate of the moving direction of a user with camera. The TTC is computed using the distance between the motion vector and the FOE.…”

Section: Introductionmentioning

confidence: 99%

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

Chae

Sun

Kang

et al. 2015

2015 IEEE International Conference on Consumer Electronics (ICCE)

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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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Optical flow based obstacle avoidance for the visually impaired

Cited by 18 publications

References 17 publications

Decision making for obstacle avoidance in autonomous mobile robots by time to contact and optical flow

Decision making for obstacle avoidance in autonomous mobile robots by time to contact and optical flow

Monocular obstacle avoidance for blind people using probabilistic focus of expansion estimation

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

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