Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach

TL;DR

This paper introduces a fast, alignment-free method for partial person re-identification using Fully Convolutional Networks and a novel Deep Spatial feature Reconstruction technique, improving accuracy without explicit alignment.

Contribution

The paper presents a new partial re-id approach combining FCN-generated spatial features with DSR to handle arbitrary partial observations without explicit alignment.

Findings

Achieves 83.58% Rank-1 accuracy on Market1501 dataset.

Outperforms several state-of-the-art partial re-id methods.

Demonstrates efficiency and effectiveness on partial person datasets.

Abstract

Partial person re-identification (re-id) is a challenging problem, where only several partial observations (images) of people are available for matching. However, few studies have provided flexible solutions to identifying a person in an image containing arbitrary part of the body. In this paper, we propose a fast and accurate matching method to address this problem. The proposed method leverages Fully Convolutional Network (FCN) to generate fix-sized spatial feature maps such that pixel-level features are consistent. To match a pair of person images of different sizes, a novel method called Deep Spatial feature Reconstruction (DSR) is further developed to avoid explicit alignment. Specifically, DSR exploits the reconstructing error from popular dictionary learning models to calculate the similarity between different spatial feature maps. In that way, we expect that the proposed FCN can decrease the similarity of coupled images from different persons and increase that from the same person. Experimental results on two partial person datasets demonstrate the efficiency and effectiveness of the proposed method in comparison with several state-of-the-art partial person re-id approaches. Additionally, DSR achieves competitive results on a benchmark person dataset Market1501 with 83.58\% Rank-1 accuracy.