Computer Vision for Learning to Interact Socially with Humans

2013 ◽  
pp. 231-256
Author(s):  
Renato Ramos da Silva ◽  
Roseli Aparecida Francelin Romero
Keyword(s):  
Computer Vision ◽  
Human Behavior ◽  
Robotic System ◽  
Social Robot ◽  
Shared Attention ◽  
Learning Mechanism ◽  
Active Mechanism ◽  
Robotic Architecture ◽  
Social Robotic

Computer vision is essential to develop a social robotic system capable to interact with humans. It is responsible to extract and represent the information around the robot. Furthermore, a learning mechanism, to select correctly an action to be executed in the environment, pro-active mechanism, to engage in an interaction, and voice mechanism, are indispensable to develop a social robot. All these mechanisms together provide a robot emulate some human behavior, like shared attention. Then, this chapter presents a robotic architecture that is composed with such mechanisms to make possible interactions between a robotic head with a caregiver, through of the shared attention learning with identification of some objects.

Computer Vision for Learning to Interact Socially with Humans

2013 ◽  
pp. 1162-1187
Author(s):  
Renato Ramos da Silva ◽  
Roseli Aparecida Francelin Romero
Keyword(s):  
Computer Vision ◽  
Human Behavior ◽  
Robotic System ◽  
Social Robot ◽  
Shared Attention ◽  
Learning Mechanism ◽  
Active Mechanism ◽  
Robotic Architecture ◽  
Social Robotic

Computer vision is essential to develop a social robotic system capable to interact with humans. It is responsible to extract and represent the information around the robot. Furthermore, a learning mechanism, to select correctly an action to be executed in the environment, pro-active mechanism, to engage in an interaction, and voice mechanism, are indispensable to develop a social robot. All these mechanisms together provide a robot emulate some human behavior, like shared attention. Then, this chapter presents a robotic architecture that is composed with such mechanisms to make possible interactions between a robotic head with a caregiver, through of the shared attention learning with identification of some objects.

Study on the Structural Model of Human Pose

2013 ◽  
Vol 464 ◽  
pp. 387-390
Author(s):  
Wei Hua Wang
Keyword(s):  
Computer Vision ◽  
Human Behavior ◽  
Structural Model ◽  
New Method ◽  
Domain Modeling ◽  
Key Technology ◽  
Broad Application ◽  
Structural Relation ◽  
Human Pose ◽  
Human Image

The analysis and understand of human behavior is broad application in the computer vision domain, modeling the human pose is one of the key technology. In order to simplify the model of the human pose and expediently describe the human pose, a lot of condition was appended to confine the process of human pose modeling or the application environments in the current research. In this paper, a new method for modeling the human pose was proposed. The human pose was modeled by the structural relation according to the physiological structural, the advantages of the model are the independent of move, the independent of scale of the human image and the dependent of view angle, it can be used to modeling the human behavior in video.

A ROBOTIC SYSTEM FOR PCBS INSPECTION BASED ON COMPUTER VISION AND MOBILE PROBES

2007 ◽  
Vol 40 (3) ◽  
pp. 171-176
Author(s):  
J. Gómez ◽  
J. Gámez ◽  
A.G. González ◽  
L. Nieto ◽  
S. Satorres ◽  
...  
Keyword(s):  
Computer Vision ◽  
Robotic System

scholarly journals Anomaly Detection for Video Surveillance

2021 ◽  
pp. 82-89
Author(s):  
Jagruti Tatiya ◽  
Riya Makhija ◽  
Mrunmay Pathe ◽  
Sarika Late ◽  
Prof. Mrunal Pathak
Keyword(s):  
Computer Vision ◽  
Anomaly Detection ◽  
Video Surveillance ◽  
Human Behavior ◽  
Low Cost ◽  
Research Area ◽  
Depth Information ◽  
Unexpected Events ◽  
Depth Sensors ◽  
Image Monitoring

Anomaly Detection is system which identifies inappropriate human behavior. One of the major problems in computer vision is identifying inappropriate human behavior. It is crucial as activity detection can help many numbers of applications. It can benefit applications like image monitoring, sign language recognization, object pursue and many more. Many alternatives are there such as low-cost depth sensors, but they do have some drawbacks such as limited indoor use also with lower resolution and clamorous depth information from deep images, it becomes nearly impossible to assess human poses. In order to resolve the above issues, the proposed system plans to utilize neural networks. One of the major research area is to recognize suspicious human behavior in video monitoring, in the field of computer vision. Several surveillance cameras are situated at places like airports, banks, bus station, malls, railway station, colleges, schools, etc to detect suspicious activities such as murder, heist, accidents, etc. It is a tedious job to detect and monitor these activities in crowded places, to trace real time human behavior and classify it into ordinary and unexpected scenarios the system needs to have a smart video surveillance. The experimental results show that the proposed methodology could assuredly detect the unexpected events in the video.

Robot Process Automation (RPA) and Its Future

2022 ◽  
pp. 35-58
Author(s):  
Ozge Doguc
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Customer Satisfaction ◽  
Human Behavior ◽  
The Other ◽  
Human Intervention ◽  
Process Automation ◽  
Robotic Process Automation ◽  
Human Agent

Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don't require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions.

Computer vision-guided robotic system for electrical power lines maintenance

1995 ◽  
Author(s):  
Jack Tremblay ◽  
T. Laliberte ◽  
Regis Houde ◽  
Michel Pelletier ◽  
Clement M. Gosselin ◽  
...  
Keyword(s):  
Computer Vision ◽  
Electrical Power ◽  
Robotic System ◽  
Power Lines

A Walking Assistant Robotic System for the Visually Impaired Based on Computer Vision and Tactile Perception

2015 ◽  
Vol 7 (5) ◽  
pp. 617-628 ◽  
Author(s):  
Dejing Ni ◽  
Aiguo Song ◽  
Lei Tian ◽  
Xiaonong Xu ◽  
Danfeng Chen
Keyword(s):  
Computer Vision ◽  
Visually Impaired ◽  
Robotic System ◽  
Tactile Perception

Foreground Detection by Competitive Learning for Varying Input Distributions

2018 ◽  
Vol 28 (05) ◽  
pp. 1750056 ◽  
Author(s):  
Ezequiel López-Rubio ◽  
Miguel A. Molina-Cabello ◽  
Rafael Marcos Luque-Baena ◽  
Enrique Domínguez
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Input Data ◽  
State Of The Art ◽  
Background Modeling ◽  
Cluster Detection ◽  
Foreground Detection ◽  
Learning Mechanism ◽  
Input Distribution ◽  
Computer Vision Applications

One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with time (e.g. changing illuminations, waving trees, water, etc.). In this work, an unsupervised learning neural network is proposed which is able to cope with progressive changes in the input distribution. It is based on a dual learning mechanism which manages the changes of the input distribution separately from the cluster detection. The proposal is adequate for scenes where the background varies slowly. The performance of the method is tested against several state-of-the-art foreground detectors both quantitatively and qualitatively, with favorable results.

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