Bayesian Tracking of Multiple Objects with Vision and Radar

被引：0
|
作者：
Hoy, Michael [1 ]
Weng, Chaoqun
Yuan, Junsong
Dauwels, Justin
机构：
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
来源：
2016 14TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV) | 2016年
关键词：
FUSION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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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页数：6
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