基于视觉和雷达的多目标贝叶斯跟踪

2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2016-11-01 DOI:10.1109/ICARCV.2016.7838788

Michael Hoy, Chaoqun Weng, Junsong Yuan, J. Dauwels

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引用次数: 5

摘要

本文研究了一种基于多个传感器信息的三维边界盒检测与跟踪系统。我们的框架是围绕一个类似于概率假设密度(PHD)过滤器的推理引擎构建的，其中状态空间由具有恒定速度动力学的随机边界盒组成。我们概述了两种模式(视觉和雷达)的测量方程。结果是一个适用于自动驾驶汽车的灵活推理系统。

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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Bayesian tracking of multiple objects with vision and radar

This paper is concerned with a system for detecting and tracking multiple 3D bounding boxes based on information from multiple sensors. Our framework is built around an inference engine similar to the probability hypothesis density (PHD) filter, where the state space consists of stochastic bounding boxes with constant velocity dynamics. We outline measurement equations for two modalities (vision and radar). The result is a flexible inference system suitable for use on autonomous vehicles.

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来源期刊

2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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