{"type":"ScholarlyArticle","title":"DenseKoopman: A Plug-and-Play Framework for Dense Pedestrian Trajectory Prediction","authors":[{"name":"Xianbang Li","slug":"xianbang-li","given":"Xianbang","family":"Li","affiliation":"State Key Lab of Intelligent Transportation System , Beihang University"},{"name":"Yilong Ren","slug":"yilong-ren","given":"Yilong","family":"Ren","affiliation":"State Key Lab of Intelligent Transportation System , Beihang University"},{"name":"Han Jiang","slug":"han-jiang","given":"Han","family":"Jiang","affiliation":"State Key Lab of Intelligent Transportation System , Beihang University"},{"name":"Haiyang Yu","slug":"haiyang-yu","given":"Haiyang","family":"Yu","affiliation":"School of Transportation Science and Engineering , Beihang University"},{"name":"Yanlei Cui","slug":"yanlei-cui","given":"Yanlei","family":"Cui","affiliation":"Hangzhou Innovation Institute , Beihang University"},{"name":"Liang Xu","slug":"liang-xu","given":"Liang","family":"Xu","affiliation":"Hangzhou Innovation Institute , Beihang University"}],"abstract":"Pedestrian trajectory prediction has emerged as a core component of human-robot interaction and autonomous driving. Fast and accurate prediction of surrounding pedestrians contributes to making decisions and improves safety and efficiency. However, pedestrians’ future trajectories will interact with their surrounding traffic participants. As the density of pedestrians increases, the complexity of such interactions also increases significantly, leading to an inevitable decrease in the accuracy of pedestrian trajectory prediction. To address this issue, we propose DenseKoopman, a plug-and-play framework for dense pedestrian trajectory prediction. Specifically, we introduce the Koopman operator theory to find an embedding space for a global linear approximation of a nonlinear pedestrian motion system. By encoding historical trajectories as linear state embeddings in the Koopman space, we transforms nonlinear trajectory data for pedestrians in dense scenes. This linearized representation greatly reduces the complexity of dense pedestrian trajectory prediction. Extensive experiments on pedestrian trajectory prediction benchmarks demonstrate the superiority of the proposed framework. We also conducted an analysis of the data transformation to explore how our DenseKoopman framework works with each validation method and uncovers motion patterns that may be hidden within the trajectory data. Code is available at https://github.com/lixianbang/DenseKoopman.","doi":"10.24963/ijcai.2024/113","url":"https://ijcai.org/proceedings/2024/113","pdf":"https://ijcai.org/proceedings/2024/0113.pdf","markdown":"https://ijcai.org/proceedings/2024/113.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/113","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":113,"pages":{"first":1020,"last":1028},"keywords":["Computer Vision: CV: Machine learning for vision","Computer Vision: CV: Motion and tracking","Computer Vision: CV: Other"],"videos":[],"references":[{"position":1,"raw":"[Abraham and Murphey, 2019] Ian Abraham and Todd D Murphey. 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