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投影卡尔曼滤波器:三维位置的多目标跟踪场景理解

Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding
课程网址: http://videolectures.net/mlmi04uk_canton_mt3lt/  
主讲教师: Cristian Ferrer Canton
开课单位: 加泰罗尼亚理工大学
开课时间: 2007-02-25
课程语种: 英语
中文简介:
本文提出了一种新方法,利用从多个校准摄像机收集的测量值来估计和跟踪场景中多个目标的3D位置。通过新构思的计算过程 - 投影卡尔曼滤波器(PKF)共同实现估计和跟踪,允许在单个统一框架中处理问题。 PKF利用观察数据的投影性质和视图之间的信息冗余来克服遮挡和空间模糊。为了证明所提算法的有效性,作者在SmartRoom场景中呈现了人们的跟踪结果,并将这些结果与现有方法进行了比较。
课程简介: This paper presents a novel approach to the problem of estimating and tracking 3D locations of multiple targets in a scene using measurements gathered from multiple calibrated cameras. Estimation and tracking is jointly achieved by a newly conceived computational process, the Projective Kalman —lter (PKF), allowing the problem to be treated in a single, uni—ed framework. The projective nature of observed data and information redundancy among views is exploited by PKF in order to overcome occlusions and spatial ambiguity. To demonstrate the e®ectiveness of the proposed algorithm, the authors present tracking results of people in a SmartRoom scenario and compare these results with existing methods as well.
关 键 词: 校准摄像机; 卡尔曼滤波器; 空间模糊
课程来源: 视频讲座网
最后编审: 2020-06-19:cxin
阅读次数: 102