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In this paper, we propose an approach that fuses information from a network of visual sensors for the analysis of human social behavior. A discriminative interaction classifier is trained based on the relative head orientation and distance between a pair of people. Specifically, we explore human interaction detection at different levels of feature fusion and decision fusion. While feature fusion mitigates...
In this paper, we propose a flexible, human-oriented framework for learning the behaviour pattern of the users in work environments from visual sensors. The knowledge of human behaviour pattern enables the ambient environment to communicate with the user in a seamless way and make anticipatory decisions, from the automation of appliances and personal schedule reminder to the detection of unhealthy...
This demonstration presents a social interaction analysis system designed to operate in real-time and under real environment conditions. A webcam is used to capture videos of a group of people interacting in an unconstrained environment. Locations of multiple people and their head poses are extracted from the videos. Direct pairwise interactions are detected based on the relative distance and head...
The goal of this work is to detect pairwise primitive interactions in groups for social interaction analysis in real environments. We propose a system that extracts locations and head poses of people from videos captured in an unconstrained environment, namely a research lab. Our system is designed to work with realistic data capturing natural human interactions. An efficient tracking method based...
In implementation, the time complexity and the performance of image fusion technique influence practicability of the products directly. In this paper, we propose an original image fusion method based on image spatial-domain features.
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