Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration

TL;DR

This paper introduces a novel event-based video person re-identification method that leverages cross-modality and temporal collaboration to enhance recognition accuracy while preserving privacy by avoiding RGB data.

Contribution

The paper proposes a Cross-Modality and Temporal Collaboration (CMTC) network that effectively utilizes event data for person ReID, addressing privacy concerns and improving performance.

Findings

Outperforms existing methods in event-based person ReID

Effectively balances event and auxiliary information

Utilizes motion and appearance cues for better recognition

Abstract

Video-based person re-identification (ReID) has become increasingly important due to its applications in video surveillance applications. By employing events in video-based person ReID, more motion information can be provided between continuous frames to improve recognition accuracy. Previous approaches have assisted by introducing event data into the video person ReID task, but they still cannot avoid the privacy leakage problem caused by RGB images. In order to avoid privacy attacks and to take advantage of the benefits of event data, we consider using only event data. To make full use of the information in the event stream, we propose a Cross-Modality and Temporal Collaboration (CMTC) network for event-based video person ReID. First, we design an event transform network to obtain corresponding auxiliary information from the input of raw events. Additionally, we propose a differential modality collaboration module to balance the roles of events and auxiliaries to achieve complementary effects. Furthermore, we introduce a temporal collaboration module to exploit motion information and appearance cues. Experimental results demonstrate that our method outperforms others in the task of event-based video person ReID.