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

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.

8 pages, 11 figures, accepted by CVPR 2018

References in corpus (3)

• Deep Residual Learning for Image Recognition
• Learning Deconvolution Network for Semantic Segmentation
• Deep Contrast Learning for Salient Object Detection

Cited by in corpus (11)

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• Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification
• Learning to Disentangle Scenes for Person Re-identification
• A Novel Teacher-Student Learning Framework For Occluded Person Re-Identification
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
• Do Different Tracking Tasks Require Different Appearance Models?
• Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
• Semi-Supervised Domain Generalizable Person Re-Identification
• Towards Discriminative Representation Learning for Unsupervised Person Re-identification
• SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
• Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification