We propose to study mobile object tracing, which allows a mobile system to report the shape, location, and trajectory of the mobile objects appearing in a video camera and identifies each of them with its cyber-identity (ID), even if the appearances of the objects are not known to the system. Existing tracking methods either cannot match objects with their cyber-IDs or rely on complex vision modules pre-learned from vast and well-annotated datasets including the appearances of the target objects, which may not exist in practice. We design and implement TagAttention, a vision-RFID fusion system that achieves mobile object tracing without the knowledge of the target object appearances and hence can be used in many applications that need to track arbitrary un-registered objects. TagAttention adopts the visual attention mechanism, through which RF signals can direct the visual system to detect and track target objects with unknown appearances. Experiments show TagAttention can actively discover, identify, and track the target objects while matching them with their cyber-IDs by using commercial sensing devices in complex environments with various multipath reflectors. It only requires around one second to detect and localize a new mobile target appearing in the video and keeps tracking it accurately over time.

中文翻译：

---

TagAttention：视觉-RFID融合的零外观知识移动对象跟踪

我们建议研究移动对象跟踪，该跟踪允许移动系统报告摄像机中出现的移动对象的形状，位置和轨迹，并使用其网络身份（ID）识别每个对象，即使这些对象的外观对象是系统未知的。现有的跟踪方法要么无法使对象与其网络ID相匹配，要么依赖于从庞大且带有良好注释的数据集中预先学习的复杂视觉模块，这些数据集包括目标对象的外观，而在实践中可能并不存在。我们设计并实现了TagAttention，这是一种视觉-RFID融合系统，可在不了解目标物体外观的情况下实现移动物体跟踪，因此可用于需要跟踪任意未注册物体的许多应用中。TagAttention采用视觉注意机制，射频信号可通过该信号引导视觉系统检测和跟踪外观未知的目标物体。实验表明，通过在复杂环境中使用具有各种多径反射器的商业传感设备，TagAttention可以主动发现，识别和跟踪目标对象，同时将其与其网络ID相匹配。只需大约一秒钟即可检测并定位视频中出现的新移动目标，并随着时间的推移不断准确地对其进行跟踪。