无需上下文信息，用自条件GAN学习轨迹的多种行为模式。
Context-free Self-Conditioned GAN for Trajectory Forecasting
- 用自条件GAN在无监督下捕捉轨迹的多模式行为特征
- 在人类运动数据上优于现有无监督方法，在道路主体数据上表现稳定
- 适合缺乏标注或标注不全的轨迹预测场景
本文提出一种基于自条件GAN的无上下文无监督方法，用于从二维轨迹中学习多种行为模式。其核心思想是：判别器特征空间中的每个模式对应一种不同的运动行为。该方法应用于轨迹预测任务，设计了三种不同的训练策略，显著提升预测性能。在人类运动和道路参与者两个数据集上进行测试，结果表明，在代表性标签最少的情况下，本方法仍优于以往无上下文方法；在人类运动数据上整体表现更优，而在道路参与者数据上也保持良好性能。
原文摘要 · Abstract (English)
In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator's feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents.
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