Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context Propagation

TL;DR

This paper introduces a cross-modality person re-identification method combining visual data and wireless positioning signals, utilizing a recurrent context propagation module to improve accuracy amidst occlusion and appearance variations.

Contribution

It proposes a novel recurrent context propagation module for integrating vision and wireless signals in person re-identification, and introduces a new WP-ReID dataset for evaluation.

Findings

The proposed method outperforms existing approaches in re-identification accuracy.

Wireless signals provide robust cues against occlusion and appearance changes.

Extensive experiments validate the effectiveness of the cross-modality approach.

Abstract

Existing person re-identification methods rely on the visual sensor to capture the pedestrians. The image or video data from visual sensor inevitably suffers the occlusion and dramatic variations of pedestrian postures, which degrades the re-identification performance and further limits its application to the open environment. On the other hand, for most people, one of the most important carry-on items is the mobile phone, which can be sensed by WiFi and cellular networks in the form of a wireless positioning signal. Such signal is robust to the pedestrian occlusion and visual appearance change, but suffers some positioning error. In this work, we approach person re-identification with the sensing data from both vision and wireless positioning. To take advantage of such cross-modality cues, we propose a novel recurrent context propagation module that enables information to propagate between visual data and wireless positioning data and finally improves the matching accuracy. To evaluate our approach, we contribute a new Wireless Positioning Person Re-identification (WP-ReID) dataset. Extensive experiments are conducted and demonstrate the effectiveness of the proposed algorithm. Code will be released at https://github.com/yolomax/WP-ReID.