2,651 research outputs found
Machine Understanding of Human Behavior
A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior
Face and Body gesture recognition for a vision-based multimodal analyser
users, computers should be able to recognize emotions, by analyzing the human's affective state, physiology and behavior. In this paper, we present a survey of research conducted on face and body gesture and recognition. In order to make human-computer interfaces truly natural, we need to develop technology that tracks human movement, body behavior and facial expression, and interprets these movements in an affective way. Accordingly in this paper, we present a framework for a vision-based multimodal analyzer that combines face and body gesture and further discuss relevant issues
Integration and coordination in a cognitive vision system
In this paper, we present a case study that exemplifies
general ideas of system integration and coordination.
The application field of assistant technology provides an
ideal test bed for complex computer vision systems including
real-time components, human-computer interaction, dynamic
3-d environments, and information retrieval aspects.
In our scenario the user is wearing an augmented reality device
that supports her/him in everyday tasks by presenting
information that is triggered by perceptual and contextual
cues. The system integrates a wide variety of visual functions
like localization, object tracking and recognition, action
recognition, interactive object learning, etc. We show
how different kinds of system behavior are realized using
the Active Memory Infrastructure that provides the technical
basis for distributed computation and a data- and eventdriven
integration approach
Multimodalities in Metadata: Gaia Gate
Metadata is information about objects. Existing metadata standards seldom describe details concerning an object’s context within an environment; this thesis proposes a new concept, external contextual metadata (ECM), examining metadata, digital photography, and mobile interface theory as context for a proposed multimodal framework of media that expresses the internal and external qualities of the digital object and how they might be employed in various use cases. The framework is binded to a digital image as a singular object. Information contained in these ‘images’ can then be processed by a renderer application to reinterpret the context that the image was captured, including non-visually. Two prototypes are developed through the process of designing a renderer for the new multimodal data framework: a proof-of-concept application and a demonstration of ‘figurative’ execution (titled ‘Gaia Gate’), followed by a critical design analysis of the resulting products
A Multimodal Perception Framework for Users Emotional State Assessment in Social Robotics
In this work, we present an unobtrusive and non-invasive perception framework based on the synergy between two main acquisition systems: the Touch-Me Pad, consisting of two electronic patches for physiological signal extraction and processing; and the Scene Analyzer, a visual-auditory perception system specifically designed for the detection of social and emotional cues. It will be explained how the information extracted by this specific kind of framework is particularly suitable for social robotics applications and how the system has been conceived in order to be used in human-robot interaction scenarios
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
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