{"title":"Experiments in estimation of independent 3D motion using EM","authors":"J. Kosecka","doi":"10.1109/AIPR.2001.991217","DOIUrl":null,"url":null,"abstract":"In this paper we address the problem of multiple 3D rigid body motion estimation from the optical flow. We use the differential epipolar constraint to measure the consistency of the local flow estimates with 3D rigid body motion and employ a probabilistic interpretation of the overall flowfield in terms of mixture models. The estimation of 3D motion parameters as well as the refinement of the initial motion segmentation is carried out using an Expectation-Maximization (EM) algorithm. The algorithm is guaranteed to improve the overall likelihood of the data. The proposed technique is a step towards estimation of 3D motion of independently moving objects in the presence of egomotion.","PeriodicalId":277181,"journal":{"name":"Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2001.991217","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

In this paper we address the problem of multiple 3D rigid body motion estimation from the optical flow. We use the differential epipolar constraint to measure the consistency of the local flow estimates with 3D rigid body motion and employ a probabilistic interpretation of the overall flowfield in terms of mixture models. The estimation of 3D motion parameters as well as the refinement of the initial motion segmentation is carried out using an Expectation-Maximization (EM) algorithm. The algorithm is guaranteed to improve the overall likelihood of the data. The proposed technique is a step towards estimation of 3D motion of independently moving objects in the presence of egomotion.
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基于EM的独立三维运动估计实验
本文研究了基于光流的三维多刚体运动估计问题。我们使用微分极面约束来测量局部流量估计与3D刚体运动的一致性,并采用混合模型对整体流场的概率解释。采用期望最大化(EM)算法对三维运动参数进行估计,并对初始运动分割进行细化。该算法保证提高数据的整体似然性。所提出的技术是在存在自我运动的情况下估计独立运动物体的三维运动的一步。
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