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arXiv · 2210.10756

Two-level Data Augmentation for Calibrated Multi-view Detection

Martin Engilberge·Haixin Shi·Zhiye Wang·Pascal Fua
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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.

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Martin Engilberge, Haixin Shi, Zhiye Wang, Pascal Fua. 2022-10-19. Two-level Data Augmentation for Calibrated Multi-view Detection. https://arxiv.org/abs/2210.10756

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