Recursive Social Behavior Graph for Trajectory Prediction

Jianhua Sun, Qinhong Jiang, Cewu Lu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 660-669

Abstract


Social interaction is an important topic in human trajectory prediction to generate plausible paths. In this paper, we present a novel insight of group-based social interaction model to explore relationships among pedestrians. We recursively extract social representations supervised by group-based annotations and formulate them into a social behavior graph, called Recursive Social Behavior Graph. Our recursive mechanism explores the representation power largely. Graph Convolutional Neural Network then is used to propagate social interaction information in such a graph. With the guidance of Recursive Social Behavior Graph, we surpass state-of-the-art methods on ETH and UCY dataset for 11.1% in ADE and 10.8% in FDE in average, and successfully predict complex social behaviors.

Related Material


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[bibtex]
@InProceedings{Sun_2020_CVPR,
author = {Sun, Jianhua and Jiang, Qinhong and Lu, Cewu},
title = {Recursive Social Behavior Graph for Trajectory Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}