...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Modelling, measurement & control,Series C.Energetics, chemistry & chemical engineering, earth resources, environmental, biomedical problems >Object Detection, Localization and Tracking-Based Deep Learning for Smart Wheelchair
【24h】

Object Detection, Localization and Tracking-Based Deep Learning for Smart Wheelchair

机译：

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

In this article, we present our work regarding the development of advanced driver-assistance systems for an electric-powered wheelchair. Our project aims at improving the autonomy of people with reduced mobility. After conducting a clinical study, we identified several use-cases. In this paper, we introduce the detection, localization and tracking of points of interest in the immediate surroundings of the chair in an indoor environment, i.e.: doors, handles, light switches, etc. The aim is not only to improve perception around the chair but also to enable semi-autonomous driving towards these targets. First, we introduce a repurposing of YOLOv3, the object detection algorithm, to our use case. Then, we show our use of the Intel Realsense camera for depth estimation. Finally, we describe our adaptation of the SORT algorithm to track 3D interest points. To validate our approach, we realized several experiments in a controlled indoor environment. The detection, distance estimation, and tracking pipeline is tested using our custom dataset. This includes corridors, doors, handles, and switches. One of the scenarios studied to validate the proposed platform includes not only the detection and tracking of objects but also the movement of the wheelchair towards one of these points of interest.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号