Deeply-Learned Part-Aligned Representations for Person Re-Identification

Abstract

In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras. We propose a simple yet effective human part-aligned representation for handling the body part misalignment problem. Our approach decomposes the human body into regions (parts) which are discriminative for person matching, accordingly computes the representations over the regions, and aggregates the similarities computed between the corresponding regions of a pair of probe and gallery images as the overall matching score. Our formulation, inspired by attention models, is a deep neural network modeling the three steps together, which is learnt through minimizing the triplet loss function without requiring body part labeling information. Unlike most existing deep learning algorithms that learn a global or spatial partition-based local representation, our approach performs human body partition, and thus is more robust to pose changes and various human spatial distributions in the person bounding box. Our approach shows state-of-the-art results over standard datasets, Market-$1501$, CUHK$03$, CUHK$01$ and VIPeR.

Accepted by ICCV 2017

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Convolutional Pose Machines
• Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts

Cited by in corpus (18)

• Improving Person Re-identification by Attribute and Identity Learning
• Deep Learning in Video Multi-Object Tracking: A Survey
• SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification
• The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching
• Improved Person Re-Identification Based on Saliency and Semantic Parsing with Deep Neural Network Models
• Domain Adaptive Person Re-Identification via Coupling Optimization
• Person Re-identification with Deep Similarity-Guided Graph Neural Network
• An Introduction to Person Re-identification with Generative Adversarial Networks
• Pose-guided Inter- and Intra-part Relational Transformer for Occluded Person Re-Identification
• End-to-End Deep Kronecker-Product Matching for Person Re-identification
• Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
• Apparel-invariant Feature Learning for Apparel-changed Person Re-identification
• Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification
• In Defense of the Classification Loss for Person Re-Identification
• Support Neighbor Loss for Person Re-Identification
• Attribute-Aware Attention Model for Fine-grained Representation Learning
• Omni-directional Feature Learning for Person Re-identification
• FTN: Foreground-Guided Texture-Focused Person Re-Identification