PoseTrackReID: Dataset Description

TL;DR

PoseTrackReID introduces a large-scale dataset that combines human pose annotations with video-based person re-identification, aiming to improve feature disentanglement and facilitate multi-person pose tracking in challenging scenarios.

Contribution

The paper presents PoseTrackReID, a novel dataset integrating pose annotations with re-ID data, bridging the gap between person re-ID and pose tracking tasks.

Findings

Provides a new benchmark for multi-frame person re-ID

Enables research on pose-informed re-identification methods

Facilitates multi-person pose tracking in occluded scenes

Abstract

Current datasets for video-based person re-identification (re-ID) do not include structural knowledge in form of human pose annotations for the persons of interest. Nonetheless, pose information is very helpful to disentangle useful feature information from background or occlusion noise. Especially real-world scenarios, such as surveillance, contain a lot of occlusions in human crowds or by obstacles. On the other hand, video-based person re-ID can benefit other tasks such as multi-person pose tracking in terms of robust feature matching. For that reason, we present PoseTrackReID, a large-scale dataset for multi-person pose tracking and video-based person re-ID. With PoseTrackReID, we want to bridge the gap between person re-ID and multi-person pose tracking. Additionally, this dataset provides a good benchmark for current state-of-the-art methods on multi-frame person re-ID.