计算机科学
跟踪(教育)
雷达
计算机视觉
人工智能
雷达跟踪器
鉴定(生物学)
极高频率
跟踪系统
实时计算
视频跟踪
钥匙(锁)
点云
计算机安全
电信
视频处理
卡尔曼滤波器
心理学
教育学
植物
生物
作者
Peijun Zhao,Chris Xiaoxuan Lu,Jianan Wang,Changhao Chen,Wei Wang,Niki Trigoni,Andrew Markham
标识
DOI:10.1109/dcoss.2019.00028
摘要
The key to offering personalised services in smart spaces is knowing where a particular person is with a high degree of accuracy. Visual tracking is one such solution, but concerns arise around the potential leakage of raw video information and many people are not comfortable accepting cameras in their homes or workplaces. We propose a human tracking and identification system (mID) based on millimeter wave radar which has a high tracking accuracy, without being visually compromising. Unlike competing techniques based on WiFi Channel State Information (CSI), it is capable of tracking and identifying multiple people simultaneously. Using a lowcost, commercial, off-the-shelf radar, we first obtain sparse point clouds and form temporally associated trajectories. With the aid of a deep recurrent network, we identify individual users. We evaluate and demonstrate our system across a variety of scenarios, showing median position errors of 0.16 m and identification accuracy of 89% for 12 people.
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