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Generation of Synthetic Images for Pedestrian Detection Using a Sequence of GANs

arXiv 2401.07370 ↗First posted 14 Jan 2024cs.CVcited by 0

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Abstract (arXiv)

Creating annotated datasets demands a substantial amount of manual effort. In this proof-of-concept work, we address this issue by proposing a novel image generation pipeline. The pipeline consists of three distinct generative adversarial networks (previously published), combined in a novel way to augment a dataset for pedestrian detection. Despite the fact that the generated images are not always visually pleasant to the human eye, our detection benchmark reveals that the results substantially surpass the baseline. The presented proof-of-concept work was done in 2020 and is now published as a technical report after a three years retention period.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 48 references that are in the map. First posted on arXiv 14 Jan 2024.

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28 of 48 references.

References · 28

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16 more
  1. 2016Playing for Data: Ground Truth from Computer Gamescited by 816
  2. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 683
  3. 2017Self-Normalizing Neural Networkscited by 591
  4. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
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  6. 2018Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomizationcited by 292
  7. 2016Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?cited by 194
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  12. 2018Context-Aware Synthesis and Placement of Object Instancescited by 41
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  15. 2017High-resolution Deep Convolutional Generative Adversarial Networkscited by 7
  16. 2019Adversarial Pixel-Level Generation of Semantic Imagescited by 1

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