Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach
This addresses the problem of identifying people from arbitrary partial body images, which is incremental as it builds on existing re-id methods with a novel alignment-free approach.
The paper tackles partial person re-identification by proposing a fast and accurate matching method that uses Fully Convolutional Networks and Deep Spatial feature Reconstruction to avoid explicit alignment, achieving 83.58% Rank-1 accuracy on the Market1501 benchmark.
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.