Face tracking and recognition by using omnidirectional sensor network

Yuzuko Utsumi, Y. Iwai
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引用次数: 2

Abstract

In recent years, security camera systems have been installed in various public facilities. More intelligent processes are needed to track people in image sequences for security camera systems. In this paper, we propose a face tracking and recognition method based on a Bayesian framework. We assume that an observed space is three-dimensional, and we estimate the 3D position of a person. We use facial 3D shape, movement, and texture models for face tracking and recognition. Omnidirectional image sensors are used to acquire image sequences of a walking person because the sensors have a wide view and are suitable for object tracking. Our system generates 3D positional hypotheses based on the facial movement model and these positional hypotheses are projected onto an image plane. Image features are extracted from projected hypotheses and the system distinguishes faces using these image features. Our evaluation experiments show that our proposed method is effective for face tracking, and that tracking accuracy is proportional to the number of cameras used.
基于全向传感器网络的人脸跟踪与识别
近年来,各种公共设施都安装了保安摄像系统。安全摄像机系统需要更智能的过程来跟踪图像序列中的人。本文提出了一种基于贝叶斯框架的人脸跟踪识别方法。我们假设观察到的空间是三维的,我们估计一个人的三维位置。我们使用面部3D形状,运动和纹理模型进行面部跟踪和识别。全向图像传感器具有视野宽、适合于目标跟踪的特点,被广泛应用于人体行走图像序列的获取。我们的系统基于面部运动模型生成3D位置假设,并将这些位置假设投影到图像平面上。从投影假设中提取图像特征,系统利用这些图像特征来识别人脸。我们的评估实验表明,我们提出的方法是有效的人脸跟踪,跟踪精度与使用的相机数量成正比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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