Kinect cane: An assistive system for the visually impaired based on three-dimensional object recognition

Takizawa, Hotaka; Yamaguchi, S.; Aoyagi, Mayumi; Ezaki, Nobuo; Mizuno, Shinji

doi:10.1109/sii.2012.6426936

Cited by 44 publications

(24 citation statements)

References 13 publications

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“…This system is able to detect dynamic obstacle and transmit information to user by audio feedback. In [21] author proposed a system using Microsoft Kinect sensor to detect 3D objects. Tactile device is used for user feedback.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Detection in Indoor Environment for Visually Impaired Using Mobile Camera

Rahman

Ullah

2018

J. Phys.: Conf. Ser.

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Abstract. Obstacle detection can improve the mobility as well as the safety of visually impaired people. In this paper, we present a system using mobile camera for visually impaired people. The proposed algorithm works in indoor environment and it uses a very simp le technique of using few p re-stored floor images. In indoor environ ment all unique floor types are considered and a single image is stored for each unique floor type. These floor images are considered as reference images. The algorithm acquires an input image frame and then a region of interest is selected and is scanned for obstacle using pre-stored floor images. The algorithm compares the present frame and the next frame and co mpute mean square error of the two frames. If mean square error is less than a threshold value α then it means that there is no obstacle in the next frame. If mean square error is greater than α then there are t wo possibilit ies; either there is an obstacle or the floor type is changed. In order to check if the floor is changed, the algorithm co mputes mean square error of next frame and all stored floor types. If minimu m of mean square error is less than a threshold value α then flour is changed otherwise there exist an obstacle. The proposed algorithm works in real-time and 96% accuracy has been achieved.

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Section: Related Workmentioning

confidence: 99%

Obstacle Detection in Indoor Environment for Visually Impaired Using Mobile Camera

Rahman

Ullah

2018

J. Phys.: Conf. Ser.

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“…We set the parameter for using SVM to find out the optimal decision rule for classification. SVM calculates w, the weight vector, by minimizing Equation (3). n  is a slack variable, and n  is added as the penalty.…”

Section: Considering Thismentioning

confidence: 99%

“…This approach reports a high accuracy across all five type of terrains types considered. In [3], an assistive system for the visually impaired was proposed. This system used Kinect sensor for identifying 3D obstacle and for classification of terrain types.…”

Section: Introductionmentioning

confidence: 99%

“…This method used single-scan laser sensor for terrain analysis which helps off-road robotic driving easy. A Support Vector Machine (SVM) based approach had been used in [1][2][3][4] for classifying terrains using visual information from an on board camera. In [12], SVM was used in addressing long range terrains classification problem where it has been proven in aiding autonomous navigation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the images of a green carpeted concrete floor and a savannah might be identified as the same color. The terrain perception systems using force sensors [2] and Kinect depth sensors [3] performed better in such scenarios. However, one of the constraints in our project is to design a robot that is less than 10 cm in diameter, thereby making use of Kinect or multiple force sensors highly impossible.…”

Section: Introductionmentioning

confidence: 99%

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Terrain Perception in a Shape Shifting Rolling-Crawling Robot

et al. 2016

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Abstract:Terrain perception greatly enhances the performance of robots, providing them with essential information on the nature of terrain being traversed. Several living beings in nature offer interesting inspirations which adopt different gait patterns according to nature of terrain. In this paper, we present a novel terrain perception system for our bioinspired robot, Scorpio, to classify the terrain based on visual features and autonomously choose appropriate locomotion mode. Our Scorpio robot is capable of crawling and rolling locomotion modes, mimicking Cebrenus Rechenburgi, a member of the huntsman spider family. Our terrain perception system uses Speeded Up Robust Feature (SURF) description method along with color information. Feature extraction is followed by Bag of Word method (BoW) and Support Vector Machine (SVM) for terrain classification. Experiments were conducted with our Scorpio robot to establish the efficacy and validity of the proposed approach. In our experiments, we achieved a recognition accuracy of over 90% across four terrain types namely grass, gravel, wooden deck, and concrete.

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