基于时空动态规划的智能房间多视角多摄像头鲁棒人脸检测

ZhenQiu Zhang, G. Potamianos, Ming Liu, Thomas S. Huang
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引用次数: 32

摘要

在真实的交互场景中,鲁棒性人脸检测提出了一个难题,为了实现这一目标,通常需要使用额外的信息源。在本文中,我们考虑了两种这样的来源:时间信息,以视频序列的形式提供;空间信息,从多个校准的相机中获得,具有感兴趣的3D场景的同步重叠视场。这两种资源被联合利用,使用一种新颖的动态规划方法,用于在适当装备的智能房间内的讲座场景,旨在在可用的2D摄像机视图中对讲师进行鲁棒的面部检测。在CHIL项目数据库上报告的实验结果表明,该方法优于纯基于帧的人脸检测
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Robust Multi-View Multi-Camera Face Detection inside Smart Rooms Using Spatio-Temporal Dynamic Programming
Robust face detection presents a difficult problem in real interaction scenarios, that, in order to achieve, most often requires employing additional sources of information. In this paper, we consider two such sources: temporal information, available in the form of video sequences, and spatial information, available from multiple calibrated cameras with synchronous, overlapping fields of view of the 3D scene of interest. These two sources are exploited jointly, using a novel dynamic programming approach, for a lecture scenario inside appropriately equipped smart rooms, aiming at robust face detection of the lecturer within the available 2D camera views. Experimental results, reported on the CHIL project database, demonstrate that the proposed approach outperforms purely frame-based face detection
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