Successive Human Tracking and Posture Estimation with Multiple Omnidirectional Cameras

Shunsuke Akama, Akihiro Matsufuji, E. Sato-Shimokawara, Shoji Yamamoto, Toru Yamaguchi
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引用次数: 1

Abstract

We propose a successive method for human tracking and posture estimation by using multiple omnidirectional cameras appropriate for Machine Learning method. A stable estimation for foot and head position is executed by the combination analysis with particle filter processing. Moreover, a classification method is accomplished by using the constraint of the connected line between head and foot position. The combination both this constraint and relative height from head to foot is possible to distinguish typical four postures for human activities in an indoor scene. We believe that this continuity of each data helps smooth convergence to the time-sequential learning for the discrimination between normal and abnormal behavior.
基于多全向摄像机的连续人体跟踪与姿态估计
我们提出了一种使用适合机器学习方法的多个全向相机进行人体跟踪和姿态估计的连续方法。采用粒子滤波结合分析的方法,实现了足、头位置的稳定估计。此外,利用头足位置连线约束实现了一种分类方法。结合这一约束和从头到脚的相对高度,可以区分室内场景中人类活动的四种典型姿势。我们认为,每个数据的这种连续性有助于平滑收敛到时间序列学习,以区分正常和异常行为。
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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