Two-level Data Augmentation for Calibrated Multi-view Detection

Published in WACV, 2023

Abstract

Data augmentation has proven its usefulness to improve model generalization and performance. While it is commonly applied in computer vision application when it comes to multi-view systems, it is rarely used. Indeed geometric data augmentation can break the alignment among views. This is problematic since multi-view data tend to be scarce and it is expensive to annotate. In this work we propose to solve this issue by introducing a new multi-view data augmentation pipeline that preserves alignment among views. Additionally to traditional augmentation of the input image we also propose a second level of augmentation applied directly at the scene level. When combined with our simple multi-view detection model, our two-level augmentation pipeline outperforms all existing baselines by a significant margin on the two main multi-view multi-person detection datasets WILDTRACK and MultiviewX.

Paper Suppl. Code

Citation

If you found this work useful, please cite the associated paper:

M. Engilberge, H. Shi, Z. Wang, and P. Fua, “Two-level Data Augmentation for Calibrated Multi-view Detection” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Jan. 2023

BibTex:

@inproceedings{engilber2023two,
  title={Two-level Data Augmentation for Calibrated Multi-view Detection},
  author={Engilberge, Martin and Shi, Haixin and Wang, Zhiye and Fua, Pascal},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  year={2023}
}