A new implementation of SGAN that exploits both real trajectoiries and synthetic trajectories obtained from JTA Dataset
This is the code for the paper:
AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
Mirko Zaffaroni,
Federico Signoretta,
Marco Grangetto,
Attilio Fiandrotti
Presented at ROAD++ @ ECCV2024
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.
Our model consists of three key components: Agumenter (A), Generator (G), and Discriminator (D). The Augmenter learns to augment synthetic trajectories into synth-augmented; the Generator learns to generate trajectories prediction; the Discriminator learns to discriminate real from generated and synth-augmented trajectories.
This code borrows from:
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Agrim Gupta,
Justin Johnson,
Fei-Fei Li,
Silvio Savarese,
Alexandre Alahi
Presented at CVPR 2018