Visual Person Identification Using a Distance-dependent Appearance Model for a Person Following Robot

Satake, Junji; Chiba, Masaya; Miura, Jun

doi:10.1007/s11633-013-0740-y

Cited by 27 publications

(31 citation statements)

References 21 publications

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“…The two main categories of visual sensors able to catch depth information are stereo and RGB-D cameras. Several works [15][16][17][18][19][20] use the first approach to approximate the distance information by triangulation methods applied on two or more RGB views of the same scene. However, the most used visual sensors for person detection are RGB-D cameras [21][22][23][24][25][26][27][28][29][30][31][32] that are able to get both RGB images and depth maps by exploiting infrared light.…”

Section: Person Followingmentioning

confidence: 99%

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

et al. 2020

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The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.

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Section: Person Followingmentioning

confidence: 99%

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

et al. 2020

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“…But from the point of view of classification, it also has some disadvantages: lack of pixels using prior information, LBP method is only focused on feature extraction, ignoring the information of the pixel itself, and this information is good or bad classification has a significant influence [5,6].…”

Section: Traditional Lbp Algorithmmentioning

confidence: 99%

The Human Face Recognition Based on the Improved Multi-scale LBP

Wang¹,

Lee²

2016

dtetr

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Abstract. This paper put forward an operator for face recognition based on LBP algorithm and multi-scale fusion method. It uses an e-LBP operator to extract texture features of face images, then divides the LBP feature of images into non-overlapping feature areas, and computes the histogram statistics on them respectively. Finally the multi-scale LBP features are derived by connecting the LBP histogram of sequences in all areas, and the nearest neighbor classifier is used for face image classification and recognition. The experimental results shows that this algorithm is able to face a good image classification and recognition and can extract much richer global information.

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“…Regarding direct approaches, different visual features have been applied to detect pedestrians, such as the stereoscopic information [ 10 ], the movement (e.g., optical flow) [ 11 ] or the appearance (e.g., local features like the Histogram of Oriented Gradients [ 12 ]). The use of this kind of histograms represents one of the most significant developments to improve algorithms that detect pedestrians.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data

Navarro

Fernández

Borraz

et al. 2016

Sensors

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This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).

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Visual Person Identification Using a Distance-dependent Appearance Model for a Person Following Robot

Cited by 27 publications

References 21 publications

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

The Human Face Recognition Based on the Improved Multi-scale LBP

A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data

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