Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of future dynamics with both diversity and accuracy. In this paper, we present a distribution discrimination (DisDis) method to predict personalized motion patterns by distinguishing the potential distributions. Motivated by that the motion pattern of each person is personalized due to his/her habit, our DisDis learns the latent distribution to represent different motion patterns and optimize it by the contrastive discrimination. This distribution discrimination encourages latent distributions to be more discriminative. Our method can be integrated with existing multi-modal stochastic predictive models as a plug-and-play module to learn the more discriminative latent distribution. To evaluate the latent distribution, we further propose a new metric, probability cumulative minimum distance (PCMD) curve, which cumulatively calculates the minimum distance on the sorted probabilities. Experimental results on the ETH and UCY datasets show the effectiveness of our method.

翻译：轨迹预测面临一个两难的困境,即用多样性和准确性来捕捉未来动态的多模式性质。在本文中,我们提出了一个分布歧视(Disdis)法,通过区分潜在分布来预测个性化运动模式。受每个人运动模式因其习惯而个性化的驱动,我们的Disdis学会了代表不同运动模式的潜在分布,并通过对比性歧视优化它。这种分布歧视鼓励潜在分布更具歧视性。我们的方法可以与现有的多模式随机预测模型相结合,作为学习更具有歧视性的潜在分布的插头和功能模块。为了评估潜在分布,我们进一步提出了一个新的指标性、概率累积最小距离曲线(PCMD),该曲线累积计算了分类概率最小距离。ET和UCY数据集的实验结果显示了我们方法的有效性。