Multi-scale Deep Learning Architectures for Person Re-identification

TL;DR

This paper introduces a multi-scale deep learning model for person re-identification that learns discriminative features at various scales and spatial locations, outperforming existing single-scale models on benchmark datasets.

Contribution

The paper proposes a novel multi-scale deep learning architecture that automatically learns the most relevant scales and spatial features for person re-identification.

Findings

Outperforms state-of-the-art on multiple benchmarks

Effectively learns scale and location importance for matching

Demonstrates robustness to appearance variations

Abstract

Person Re-identification (re-id) aims to match people across non-overlapping camera views in a public space. It is a challenging problem because many people captured in surveillance videos wear similar clothes. Consequently, the differences in their appearance are often subtle and only detectable at the right location and scales. Existing re-id models, particularly the recently proposed deep learning based ones match people at a single scale. In contrast, in this paper, a novel multi-scale deep learning model is proposed. Our model is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching. The importance of different spatial locations for extracting discriminative features is also learned explicitly. Experiments are carried out to demonstrate that the proposed model outperforms the state-of-the art on a number of benchmarks