Robust visual line-following navigation system for humanoid robots

Juang, Li-Hong; Zhang, Jian-Sen

doi:10.1007/s10462-018-9672-9

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…Fingerprints are easy to obtain and use, reliable, miniaturized, inexpensive, and cost-effective. However, fingerprints are affected by skin and humidity, and fingerprints can be easily copied, for example, by candles or playday [3]. Face recognition is now widely used and relatively mature, through the captured facial image or video extraction and analysis of the characteristics of the entire face and its contours to achieve personal recognition.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Privacy‐Oriented Successive Approximation Image Position Follower Processing

Miao

Shao²,

Yan³

2021

Complexity

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In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Privacy‐Oriented Successive Approximation Image Position Follower Processing

Miao

Shao²,

Yan³

2021

Complexity

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“…1. The guide parameters as the primary inputs of the controller comprise the angle deviation and position deviation [25]. The controller outputs the control directives based on the algorithm of the controller to make the robot change its attitude; angle deviation and position deviation changes accordingly.…”

Section: A Basic Theorymentioning

confidence: 99%

Visual Tracking Control of Humanoid Robot

Juang

Zhang

2019

IEEE Access

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This paper realizes the independent complex route tracking based on the NAO robot hardware platform and an image processing technology. The camera captures an image and extracts a path through a series of image processes, such as threshold, filtering, and edge detection. The path recognition algorithms and its edge array were used to calculate these relative parameters that drive the anthropomorphic robot for complex route walking for autonomous tracking. These paths include the straight-route curves and the cross routes, which were identified by the different features accurately. Particularly, the slope-matching method is proposed to enable it to track in accordance with the established rules when meeting the cross routes. These methods separately calculate the slope from the intersection to paths in all directions, and then, the matching rate can be obtained by a predefined matching formula in terms of choosing the best path forward. All complex route tracking experiments were conducted by an anthropomorphic robot. The experimental results show that the robot imposed a strong anti-interference ability to filter out the noise and have accurate complex route tracking, which is significant for the anthropomorphic robot visual guide. INDEX TERMS Complex route tracking, anthropomorphic robot, image processing, path recognition, autonomous tracking, visual guide.

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“…No matter it is to predict the vehicle's moving trajectories [23] or to detect front vehicles [24], [25], lane marking detection can always act as an aid. In addition to intelligent driving, lane marking detection also contributes to robot navigation [26] and provides auxiliary information for visually impaired people's motion [27].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Lane Marking Detection: A Survey

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm.

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Robust visual line-following navigation system for humanoid robots

Cited by 15 publications

References 28 publications

[Retracted] Privacy‐Oriented Successive Approximation Image Position Follower Processing

[Retracted] Privacy‐Oriented Successive Approximation Image Position Follower Processing

Visual Tracking Control of Humanoid Robot

Deep Learning in Lane Marking Detection: A Survey

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