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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Variational Inference for 3-D Localization and Tracking of Multiple Targets Using Multiple Cameras
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Variational Inference for 3-D Localization and Tracking of Multiple Targets Using Multiple Cameras

机译：使用多台摄像机进行3-D定位和跟踪多个目标的变分推理

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摘要

This paper proposes a novel unified framework to solve the 3-D localization and tracking problem that occurs multiple camera settings with overlapping views. The main challenge is to overcome the uncertainty of the back projection arising from the challenges of ground point detection in an environment that includes severe occlusions and the unknown heights of people. To tackle this challenge, we establish a Bayesian learning framework that maximizes a posterior over the trajectory assignments and 3-D positions for given detections from multiple cameras. To solve the Bayesian learning problem in a tractable form, we develop an expectation-maximization scheme based on the variation inference approximation, where the probability distributions are designed to follow Boltzmann distributions of seven terms that are induced from multicamera tracking settings. The experimental results show that the proposed method outperforms the state-of-the-art methods on the challenging multicamera data sets.
机译：本文提出了一个新颖的统一框架来解决3-D定位和跟踪问题，这种问题会在多个相机设置重叠的视图中发生。主要挑战是克服在严重遮挡和人员未知高度的环境中进行地面点检测所带来的背投不确定性。为了解决这一挑战，我们建立了一个贝叶斯学习框架，该框架可以最大化从多个摄像机进行给定检测的轨迹分配和3-D位置的后验。为了以可解决的形式解决贝叶斯学习问题，我们基于变化推断近似开发了一种期望最大化方案，其中概率分布被设计为遵循从多相机跟踪设置中得出的七个项的玻尔兹曼分布。实验结果表明，在具有挑战性的多相机数据集上，该方法的性能优于最新方法。

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