19,430 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
When Computer Vision Gazes at Cognition
Joint attention is a core, early-developing form of social interaction. It is
based on our ability to discriminate the third party objects that other people
are looking at. While it has been shown that people can accurately determine
whether another person is looking directly at them versus away, little is known
about human ability to discriminate a third person gaze directed towards
objects that are further away, especially in unconstraint cases where the
looker can move her head and eyes freely. In this paper we address this
question by jointly exploring human psychophysics and a cognitively motivated
computer vision model, which can detect the 3D direction of gaze from 2D face
images. The synthesis of behavioral study and computer vision yields several
interesting discoveries. (1) Human accuracy of discriminating targets
8{\deg}-10{\deg} of visual angle apart is around 40% in a free looking gaze
task; (2) The ability to interpret gaze of different lookers vary dramatically;
(3) This variance can be captured by the computational model; (4) Human
outperforms the current model significantly. These results collectively show
that the acuity of human joint attention is indeed highly impressive, given the
computational challenge of the natural looking task. Moreover, the gap between
human and model performance, as well as the variability of gaze interpretation
across different lookers, require further understanding of the underlying
mechanisms utilized by humans for this challenging task.Comment: Tao Gao and Daniel Harari contributed equally to this wor
A distributed camera system for multi-resolution surveillance
We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor.
Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database.
Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table.
We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating
under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance
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