论文标题
场景门控社交图：基于动态社交图和场景约束的行人轨迹预测
Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints
论文作者
论文摘要
行人轨迹预测对于理解人类运动行为是有价值的，由于其他行人的社会影响，场景的限制和预测轨迹的多模式可能性，这是一项挑战。大多数现有方法仅关注以上三个关键元素中的两个。为了共同考虑所有这些元素，我们提出了一种名为“场景门控社会图”（SGSG）的新型轨迹预测方法。在拟议的SGSG中，动态图用于描述行人之间的社会关系。社交和场景的影响通过场景封闭的社交图形特征考虑在内，这些特征结合了编码的社交图和语义场景特征。此外，还合并了一个VAE模块，以了解场景封闭式社会特征和示例潜在变量，以生成在社会和环境上可以接受的多个轨迹。我们将SGSG与二十个最先进的行人轨迹预测方法进行了比较，结果表明，所提出的方法在两个广泛使用的轨迹预测基准上实现了卓越的性能。
Pedestrian trajectory prediction is valuable for understanding human motion behaviors and it is challenging because of the social influence from other pedestrians, the scene constraints and the multimodal possibilities of predicted trajectories. Most existing methods only focus on two of the above three key elements. In order to jointly consider all these elements, we propose a novel trajectory prediction method named Scene Gated Social Graph (SGSG). In the proposed SGSG, dynamic graphs are used to describe the social relationship among pedestrians. The social and scene influences are taken into account through the scene gated social graph features which combine the encoded social graph features and semantic scene features. In addition, a VAE module is incorporated to learn the scene gated social feature and sample latent variables for generating multiple trajectories that are socially and environmentally acceptable. We compare our SGSG against twenty state-of-the-art pedestrian trajectory prediction methods and the results show that the proposed method achieves superior performance on two widely used trajectory prediction benchmarks.
