Robust Human Pose Tracking For Realistic Service Robot Applications

Vasileiadis, Manolis; Malassiotis, Sotiris; Giakoumis, Dimitrios; Bouganis, Christos-Savvas; Tzovaras, Dimitrios

doi:10.1109/iccvw.2017.162

Cited by 18 publications

(8 citation statements)

References 41 publications

(59 reference statements)

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“…The rest runs in real time. In terms of computer resources, a computer with advanced hardware is used in [ 76 ], as well as robotic platforms such as MOBOT [ 77 ], Hobbit [ 80 ], Roomba [ 81 ], and a customized robot [ 78 ].…”

Section: Discussion and Conclusionmentioning

confidence: 99%

“…On the other hand, Vasileiadis et al in [ 76 ] proposed an algorithm for an assistive robot to track the body pose, which is part of the Horizon 2020 program to develop the RAMCIP robot. First, the skeleton and joints are extracted utilizing a depth camera.…”

Section: Algorithms Used For the Bodymentioning

confidence: 99%

“…For algorithms for monitoring, the most popular application is fall detection. However, the algorithm proposed in [ 76 ] to recognize the body postures of ADLs has the best performance reported (97.4%), employing an SDF model. The authors used a workstation with a GPU to achieve this performance.…”

Section: Algorithms Used For the Bodymentioning

confidence: 99%

See 2 more Smart Citations

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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Section: Discussion and Conclusionmentioning

confidence: 99%

Section: Algorithms Used For the Bodymentioning

confidence: 99%

See 1 more Smart Citation

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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show abstract

“…According to the parts of human body, the position of the human joint was estimated. Vasileiadis et al [ 33 ] used 3D Signed Distance Functions (SDF) data to represent the model, which was extended by a supplementary mechanism to track the pose of the human body in the depth sequence. In the actual multi-person interaction scene, the depth data of the human body in different perspectives was collected [ 34 ], and the mesh model was used for fitting to eliminate contact joint error.…”

Section: Methods Of Point Cloud-based Joint Estimationmentioning

confidence: 99%

A Review: Point Cloud-Based 3D Human Joints Estimation

Jia

et al. 2021

Sensors

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Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.

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“…Therefore, in order to ensure constant situation awareness about the human presence, we fused two specific human tracking algorithms by exploiting data form robot's laser scanners and RGB-D sensor: RGB-D based human tracking. A human detection and tracking framework suitable to operate with low-cost depth sensors at real-life situations addressing limitations such as body part occlusions, partialbody views, sensor noise and interaction with objects [54] has been adopted. In particular, a human template is initialized in the first frame of a tracking sequence, through a two-step initialization process, using as input the human pose estimation provided by the Microsoft Kinect v1 built-in skeleton tracker [46].…”

Section: Global Human Observation In the Scenementioning

confidence: 99%

Understanding of Human Behavior with a Robotic Agent Through Daily Activity Analysis

Kostavelis

Vasileiadis

Skartados

et al. 2019

Int J of Soc Robotics

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Personal assistive robots to be realized in the near future should have the ability to seamlessly coexist with humans in unconstrained environments, with the robot's capability to understand and interpret the human behavior during human-robot cohabitation significantly contributing towards this end. Still, the understanding of human behavior through a robot is a challenging task as it necessitates a comprehensive representation of the high-level structure of the human's behavior from the robot's low-level sensory input. The paper at hand tackles this problem by demonstrating a robotic agent capable of apprehending human daily activities through a method, the Interaction Unit analysis, that enables activities' decomposition into a sequence of units, each one associated with a behavioral factor. The modelling of human behavior is addressed with a Dynamic Bayesian Network that operates on top of the Interaction Unit, offering quantification of the behavioral factors and the formulation of the human's behavioral model. In addition, lightweight human action and object manipulation monitoring strategies have been developed, based on RGB-D and laser sensors, tailored for onboard robot operation. As a proof of concept, we used our robot to evaluate the ability of the method to differentiate among the examined human activities, as well as to assess the capability of behavior modeling of people with Mild Cognitive Impairment. Moreover, we deployed our robot in 12 real

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Robust Human Pose Tracking For Realistic Service Robot Applications

Cited by 18 publications

References 41 publications

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

A Review: Point Cloud-Based 3D Human Joints Estimation

Understanding of Human Behavior with a Robotic Agent Through Daily Activity Analysis

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