A novel obstacle detection method based on deformable grid for the visually impaired

Kang, Mun-Cheon; Chae, Sung-Ho; Sun, Jee-Young; Yoo, Jinwoo; Ko, Sung-Jea

doi:10.1109/tce.2015.7298298

Cited by 38 publications

(8 citation statements)

References 20 publications

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“…13 depicts the number of research papers published in the various areas against the different steps included in obstacle detection from year 1998 to 2015. After analyzing all the research carried in various fields, conclusions are presented like in field of monocular vision based approach, it is concluded that deformable grid proposed by Kang et al [18] is a good method for detecting obstacles at the risk of collision, where as to evaluate distinction between ground plane and obstacle, two successive frames prototype proposed by Jia et al [17] comes out to be appropriate as no calibration or any prior knowledge of the camera is required and is cost effective also. For detecting and tracking moving obstacles, in-motion detection method proposed by Mishra et al [14] is concluded to be the appropriate one as it is computationally simple having better time response.…”

Section: Results and Conclusionmentioning

confidence: 99%

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

Singh¹,

Kaur²

2017

IJIGSP

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Abstract-Obstacle detection is the process in which the upcoming objects in the path are detected and collision with them is avoided by some sort of signalling to the visually impaired person. In this review paper we present a comprehensive and critical survey of Image Processing techniques like vision based, ground plane detection, feature extraction, etc. for detecting the obstacles. Two types of vision based techniques namely (a) Monocular vision based approach (b) Stereo Vision based approach are discussed. Further types of above described approaches are also discussed in the survey. Survey discusses the analysis of the associated work reported in literature in the field of SURF and SIFTS features, monocular vision based approaches, texture features and ground plane obstacle detection.

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Section: Results and Conclusionmentioning

confidence: 99%

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

Singh¹,

Kaur²

2017

IJIGSP

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“…Pham et al [21] used the Kinect sensor to collect the color depth data in front of the user, and then used the 3D sensing OpenNI framework to process and extract accurate 3D information of the obstacles by the PCL library. Kang et al [9] adopted a DG (Deformable Grid) method to detect obstacles in order to help persons with visual impairment. DG initially has a regular grid shape.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the characteristics of radio waves, this method only locates the positions or recognizes the behaviors of obstacles, and usually need complex equipment. In the image-based method, monocular or binocular cameras are usually used to capture the surrounding environment, and then machine learning algorithms (e.g., the deformable grid method [9], the convolutional neural network [10], the image segmentation [11], the support vector machine (SVM) [12], or the Markov chain [13], etc.) are used to process and extract the information of obstacles in the images, thereby locating the position and size of the obstacle.…”

Section: Introductionmentioning

confidence: 99%

A Position and Area Localization Algorithm for Obstacles in the Environment of Sparsely-Deployed Sensors

Gao

Yang

et al. 2021

IEEE Access

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Measuring the space area of obstacles is one of the important problems in obstacle localizing fields. Most of the existing research works on the localization of obstacles focus on where the obstacles are, and few of them measure both the positions and the areas of the obstacles. In this paper, we propose a Minimum convex bounding Polygon localizing algorithm based on Visible light Tracking (MPVT) in order to rapidly and accurately locate the position and area of a 2D obstacle in the environment of sparsely-deployed sensors. MPVT first determines the initial localizing light by Visible Light Tracing method (VLT). Second, it searches the first side of the Minimum Convex Bounding Polygon (MCBP) of the obstacle. Third, MPVT calculates the subsequent other sides and the vertexes of MCBP until the next side coincides with the first side. In order to evaluate the approximation degree between the actual values and the localization values in terms of areas, positions and shapes, we propose two performance evaluation indexes, i.e., the area ratio and the ratio of equivalent radius. We conducted experiments on the influence of obstacle orientation and sparseness of sensor deployment, the accuracy comparison with the existing methods, and the time complexity. Experiment results show that MPVT can accurately locate the position and area of the obstacle in the environment of sparsely-deployed sensors with low time overhead, and is suitable for low-cost obstacle localization applications. INDEX TERMS Minimum convex bounding polygon, obstacle localization, ratio of equivalent radius, sparse environment, visible light tracking

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“…A wearable device is designed using a haptic strap, sensor belt, and vibration motors to detect different types of obstacles and allow for safe navigation [ 18 ]. An object-detection method is proposed in [ 19 ], which is based on a deformable grid (DG), which depends upon the motion of an object and can detect the risk of collision.…”

Section: Introductionmentioning

confidence: 99%

Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People

Joshi

Yadav

Dutta

et al. 2020

Entropy

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Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time.

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A novel obstacle detection method based on deformable grid for the visually impaired

Cited by 38 publications

References 20 publications

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

A Position and Area Localization Algorithm for Obstacles in the Environment of Sparsely-Deployed Sensors

Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People

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