Unsupervised Noisy Tracklet Person Re-identification

TL;DR

This paper introduces an unsupervised, noise-robust tracklet learning method for person re-identification that eliminates the need for labeled data and outperforms existing methods on large benchmarks.

Contribution

The paper proposes a novel selective tracklet learning approach that trains discriminative re-id models from unlabelled, noisy tracklet data, enhancing scalability and robustness.

Findings

Outperforms state-of-the-art unsupervised re-id methods

Robust against noisy and unbalanced tracklet data

Effective on large-scale re-id benchmarks

Abstract

Existing person re-identification (re-id) methods mostly rely on supervised model learning from a large set of person identity labelled training data per domain. This limits their scalability and usability in large scale deployments. In this work, we present a novel selective tracklet learning (STL) approach that can train discriminative person re-id models from unlabelled tracklet data in an unsupervised manner. This avoids the tedious and costly process of exhaustively labelling person image/tracklet true matching pairs across camera views. Importantly, our method is particularly more robust against arbitrary noisy data of raw tracklets therefore scalable to learning discriminative models from unconstrained tracking data. This differs from a handful of existing alternative methods that often assume the existence of true matches and balanced tracklet samples per identity class. This is achieved by formulating a data adaptive image-to-tracklet selective matching loss function explored in a multi-camera multi-task deep learning model structure. Extensive comparative experiments demonstrate that the proposed STL model surpasses significantly the state-of-the-art unsupervised learning and one-shot learning re-id methods on three large tracklet person re-id benchmarks.