GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval

Abstract

The huge variance of human pose and the misalignment of detected human images significantly increase the difficulty of person Re-Identification (Re-ID). Moreover, efficient Re-ID systems are required to cope with the massive visual data being produced by video surveillance systems. Targeting to solve these problems, this work proposes a Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework, respectively. GLAD explicitly leverages the local and global cues in human body to generate a discriminative and robust representation. It consists of part extraction and descriptor learning modules, where several part regions are first detected and then deep neural networks are designed for representation learning on both the local and global regions. A hierarchical indexing and retrieval framework is designed to eliminate the huge redundancy in the gallery set, and accelerate the online Re-ID procedure. Extensive experimental results show GLAD achieves competitive accuracy compared to the state-of-the-art methods. Our retrieval framework significantly accelerates the online Re-ID procedure without loss of accuracy. Therefore, this work has potential to work better on person Re-ID tasks in real scenarios.

Accepted by ACM MM2017, 9 pages, 5 figures

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• DeepPose: Human Pose Estimation via Deep Neural Networks
• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
• A Discriminatively Learned CNN Embedding for Person Re-identification
• Convolutional Pose Machines
• Deep Attributes Driven Multi-Camera Person Re-identification
• PersonNet: Person Re-identification with Deep Convolutional Neural Networks
• Pose Invariant Embedding for Deep Person Re-identification

Cited by in corpus (73)

• A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification
• Deep Representation Learning with Part Loss for Person Re-Identification
• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
• SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification
• Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
• Bag of Tricks and A Strong Baseline for Deep Person Re-identification
• Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
• Omni-Scale Feature Learning for Person Re-Identification
• Second-order Non-local Attention Networks for Person Re-identification
• Multi-task Learning with Coarse Priors for Robust Part-aware Person Re-identification
• ATRW: A Benchmark for Amur Tiger Re-identification in the Wild
• TransReID: Transformer-based Object Re-Identification
• Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification
• Joint Discriminative and Generative Learning for Person Re-identification
• Learning Multi-Attention Context Graph for Group-Based Re-Identification
• ABD-Net: Attentive but Diverse Person Re-Identification
• Horizontal Pyramid Matching for Person Re-identification
• Global-Local Temporal Representations For Video Person Re-Identification
• Let Features Decide for Themselves: Feature Mask Network for Person Re-identification
• Learning Generalisable Omni-Scale Representations for Person Re-Identification
• Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
• Interaction-and-Aggregation Network for Person Re-identification
• Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective
• Domain Adaptive Person Re-Identification via Coupling Optimization
• Identity-Guided Human Semantic Parsing for Person Re-Identification
• EANet: Enhancing Alignment for Cross-Domain Person Re-identification
• Learning Shape Representations for Clothing Variations in Person Re-Identification
• Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
• Batch Coherence-Driven Network for Part-aware Person Re-Identification
• Spatiotemporal Transformer for Video-based Person Re-identification
• Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
• Pose-Normalized Image Generation for Person Re-identification
• Multi-scale 3D Convolution Network for Video Based Person Re-Identification
• Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-Identification
• Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification
• AANet: Attribute Attention Network for Person Re-Identifications
• Batch DropBlock Network for Person Re-identification and Beyond
• CA3Net: Contextual-Attentional Attribute-Appearance Network for Person Re-Identification
• CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
• Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
• Densely Semantically Aligned Person Re-Identification
• Spatial and Temporal Mutual Promotion for Video-based Person Re-identification
• Spectral Feature Transformation for Person Re-identification
• Real-world Person Re-Identification via Degradation Invariance Learning
• Semantics-Aligned Representation Learning for Person Re-identification
• In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation
• STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification
• Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians
• Learning Context Graph for Person Search
• Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New Benchmark
• Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization
• GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification
• Discovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identification
• Apparel-invariant Feature Learning for Apparel-changed Person Re-identification
• Hybrid-Attention Guided Network with Multiple Resolution Features for Person Re-Identification
• Robust Partial Matching for Person Search in the Wild
• Pedestrian re-identification based on Tree branch network with local and global learning
• UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification
• Collaborative Attention Network for Person Re-identification
• Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification
• In Defense of the Classification Loss for Person Re-Identification
• Angular Triplet Loss-based Camera Network for ReID
• STADB: A Self-Thresholding Attention Guided ADB Network for Person Re-identification
• Attention Driven Person Re-identification
• Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification
• GAN-based Pose-aware Regulation for Video-based Person Re-identification
• Query Adaptive Late Fusion for Image Retrieval
• Cross-Correlated Attention Networks for Person Re-Identification
• SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
• Learning Deep Representations by Mutual Information for Person Re-identification
• End-to-End Training of CNN Ensembles for Person Re-Identification
• Single Camera Training for Person Re-identification
• RAM: A Region-Aware Deep Model for Vehicle Re-Identification