One-pass Person Re-identification by Sketch Online Discriminant Analysis

TL;DR

This paper introduces an online person re-identification method called SoDA that efficiently updates the model with each new data sample without storing all past data, suitable for large-scale multi-camera systems.

Contribution

We propose a novel Sketch online Discriminant Analysis (SoDA) that approximates Fisher discriminant analysis in an online setting without extensive data storage.

Findings

SoDA achieves competitive re-id accuracy in online scenarios.

Theoretical bounds demonstrate how SoDA approximates offline FDA.

Experimental results validate the efficiency and effectiveness of SoDA.

Abstract

Person re-identification (re-id) is to match people across disjoint camera views in a multi-camera system, and re-id has been an important technology applied in smart city in recent years. However, the majority of existing person re-id methods are not designed for processing sequential data in an online way. This ignores the real-world scenario that person images detected from multi-cameras system are coming sequentially. While there is a few work on discussing online re-id, most of them require considerable storage of all passed data samples that have been ever observed, and this could be unrealistic for processing data from a large camera network. In this work, we present an onepass person re-id model that adapts the re-id model based on each newly observed data and no passed data are directly used for each update. More specifically, we develop an Sketch online Discriminant Analysis (SoDA) by embedding sketch processing into Fisher discriminant analysis (FDA). SoDA can efficiently keep the main data variations of all passed samples in a low rank matrix when processing sequential data samples, and estimate the approximate within-class variance (i.e. within-class covariance matrix) from the sketch data information. We provide theoretical analysis on the effect of the estimated approximate within-class covariance matrix. In particular, we derive upper and lower bounds on the Fisher discriminant score (i.e. the quotient between between-class variation and within-class variation after feature transformation) in order to investigate how the optimal feature transformation learned by SoDA sequentially approximates the offline FDA that is learned on all observed data. Extensive experimental results have shown the effectiveness of our SoDA and empirically support our theoretical analysis.