Robust RFID-Based Multi-Object Identification and Tracking with Visual Aids

Abstract

Obtaining fine-grained spatial information is of practical importance in RFID-based applications. However, high-precision positioning remains a challenging task in commercial-off-the-shelf (COTS) RFID systems. Inspired by progress in the computer vision (CV) field, researchers propose to combine CV with RFID systems and turn the positioning problem into a matching problem. Promising though it seems, current methods fuse CV and RFID through converting traces of tagged objects extracted from videos by CV into phase sequences for matching, which is a dimension-reduced procedure causing loss of spatial resolution. Consequently, they fail in more harsh conditions such as small tag intervals and low reading rates of tags. To address the limitation, we propose TagFocus, a more robust RFID-enabled system for fine-grained multi-object identification and tracking with visual aids. The key observation of TagFocus is that traces generated by different methods shall be compatible if they are acquired from one identical object. Leveraging this observation, an attention-based sequence-to-sequence (seq2seq) model is trained to generate a simulated trace for each candidate tag-object pair. And the trace of the right pair shall best match the observed trace directly extracted by CV. A prototype of TagFocus is implemented and extensively assessed in lab environments. Experimental results show that our system maintains a matching accuracy of over 89% in harsh conditions, outperforming state-of-the-art schemes by 25%.