Learning Feature Aggregation in Temporal Domain for Re-Identification

• Main Authors: Špaňhel, Jakub, Sochor, Jakub, Juránek, Roman, Dobeš, Petr, Bartl, Vojtěch, Herout, Adam
• Format: Journal Article
• Language: English
• Published: 12-03-2019
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.1903.05244

Person re-identification is a standard and established problem in the
computer vision community. In recent years, vehicle re-identification is also
getting more attention. In this paper, we focus on both these tasks and propose
a method for aggregation of features in temporal domain as it is common to have
multiple observations of the same object. The aggregation is based on weighting
different elements of the feature vectors by different weights and it is
trained in an end-to-end manner by a Siamese network. The experimental results
show that our method outperforms other existing methods for feature aggregation
in temporal domain on both vehicle and person re-identification tasks.
Furthermore, to push research in vehicle re-identification further, we
introduce a novel dataset CarsReId74k. The dataset is not limited to
frontal/rear viewpoints. It contains 17,681 unique vehicles, 73,976 observed
tracks, and 277,236 positive pairs. The dataset was captured by 66 cameras from
various angles.