Spatial-Temporal Person Re-identification
arXiv:1812.03282
Abstract
Most of current person re-identification (ReID) methods neglect a spatial-temporal constraint. Given a query image, conventional methods compute the feature distances between the query image and all the gallery images and return a similarity ranked table. When the gallery database is very large in practice, these approaches fail to obtain a good performance due to appearance ambiguity across different camera views. In this paper, we propose a novel two-stream spatial-temporal person ReID (st-ReID) framework that mines both visual semantic information and spatial-temporal information. To this end, a joint similarity metric with Logistic Smoothing (LS) is introduced to integrate two kinds of heterogeneous information into a unified framework. To approximate a complex spatial-temporal probability distribution, we develop a fast Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint, the st-ReID model eliminates lots of irrelevant images and thus narrows the gallery database. Without bells and whistles, our st-ReID method achieves rank-1 accuracy of 98.1\% on Market-1501 and 94.4\% on DukeMTMC-reID, improving from the baselines 91.2\% and 83.8\%, respectively, outperforming all previous state-of-the-art methods by a large margin.
AAAI 2019
References in corpus (8)
- Improving Person Re-identification by Attribute and Identity Learning
- Random Erasing Data Augmentation
- Pedestrian Alignment Network for Large-scale Person Re-identification
- Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
- SVDNet for Pedestrian Retrieval
- Joint Person Re-identification and Camera Network Topology Inference in Multiple Cameras
- M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification
- Occluded Person Re-identification