AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912396799377408 |
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| author | Zaffaroni, Mirko Signoretta, Federico Grangetto, Marco Fiandrotti, Attilio |
| author_facet | Zaffaroni, Mirko Signoretta, Federico Grangetto, Marco Fiandrotti, Attilio |
| contents | Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. Deep generative models achieve top performance in this task, assuming enough labelled trajectories are available for training. To this end, large amounts of synthetically generated, labelled trajectories exist (e.g., generated by video games). However, such trajectories are not meant to represent pedestrian motion realistically and are ineffective at training a predictive model. We propose a method and an architecture to augment synthetic trajectories at training time and with an adversarial approach. We show that trajectory augmentation at training time unleashes significant gains when a state-of-the-art generative model is evaluated over real-world trajectories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18038 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data Zaffaroni, Mirko Signoretta, Federico Grangetto, Marco Fiandrotti, Attilio Computer Vision and Pattern Recognition Artificial Intelligence Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. Deep generative models achieve top performance in this task, assuming enough labelled trajectories are available for training. To this end, large amounts of synthetically generated, labelled trajectories exist (e.g., generated by video games). However, such trajectories are not meant to represent pedestrian motion realistically and are ineffective at training a predictive model. We propose a method and an architecture to augment synthetic trajectories at training time and with an adversarial approach. We show that trajectory augmentation at training time unleashes significant gains when a state-of-the-art generative model is evaluated over real-world trajectories. |
| title | AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.18038 |