Dynamic context driven human detection and tracking in meeting scenarios

Abstract

As a significant part of context-aware systems, human-centered visual processing is required to be adaptive and interactive within dynamic context in real-life situation. In this paper a novel bottom-up and top-down integrated approach is proposed to solve the problem of dynamic context driven visual processing in meeting scenarios. A set of visual detection, tracking and verification modules are effectively organized to extract rough-level visual information, based on which a bottom-up context analysis is performed through Bayesian Network. In reverse, results of scene analysis are applied as top-down guidance to control refined level visual processing. The system has been tested under real-life meeting environment that includes three typical scenarios: speech, discussion and meeting break. The experiments show the effectiveness and robustness of our approach within continuously changing meeting scenarios and dynamic context.