A Real-Time Vehicle Detection Algorithm Based on Sparse Point Clouds and Dempster-Shafer Fusion Theory
文献类型：会议论文
| 作者 | Xu FY(徐凤煜)1,2; Liang HW(梁华为)2,5 ; Wang ZL(王智灵)2,5; Lin LL(林玲龙)2,4; Chu ZW(储志伟)3
|
| 出版日期 | 2019-08-26 |
| 会议日期 | 2018-8-11 |
| 关键词 | vehicle detection sparse point clouds virtual laser line D-S fusion |
| 英文摘要 | In order to improve real-time performance and
reduce the dependence on computing resources, we present a
novel vehicle detection algorithm based on sparse point clouds
in this paper. In point clouds segmentation, virtual laser line is
proposed and our fast two-step segmentation method has proved
to be time-effificient. Since accuracy and real-time capabilities are
all crucial for autonomous vehicles, we utilize multiple features
analysis and dempster-shafer(D-S) fusion theory to improve
detection accuracy. Validation tests and experimental results
show our method has a high performance in real urban traffific
situations. |
| 会议录 | 2018 IEEE International Conference on Information and Automation (ICIA)
 |
| 语种 | 英语 |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/125914]  |
| 专题 | 合肥物质科学研究院_中科院合肥智能机械研究所 |
| 作者单位 | 1.中国科学技术大学 2.中科学院合肥物质科学研究院 3.东北大学 4.中国科学院机器人与智能制造创新研究院 5.安徽省智能驾驶技术及应用工程实验室 |
| 推荐引用方式 GB/T 7714 | Xu FY,Liang HW,Wang ZL,et al. A Real-Time Vehicle Detection Algorithm Based on Sparse Point Clouds and Dempster-Shafer Fusion Theory[C]. 见:. 2018-8-11. |
入库方式： OAI收割
来源：合肥物质科学研究院
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