{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ss-lstm-a-hierarchical-lstm-model-for","title":"SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction","arxiv_id":null,"date":"2018-03-12","proceeding":"IEEE Winter Conference on Applications of Computer Vision 2018 3","authors":["Hao Xue Du Q. Huynh Mark Reynolds"],"abstract":"Pedestrian trajectory prediction is an extremely challenging problem because of the crowdedness and clutter of\r\nthe scenes. Previous deep learning LSTM-based approaches focus on the neighbourhood influence of pedestrians\r\nbut ignore the scene layouts in pedestrian trajectory prediction. In this paper, a novel hierarchical LSTM-based network is proposed to consider both the influence of social\r\nneighbourhood and scene layouts. Our SS-LSTM, which\r\nstands for Social-Scene-LSTM, uses three different LSTMs\r\nto capture person, social and scene scale information. We\r\nalso use a circular shape neighbourhood setting instead of\r\nthe traditional rectangular shape neighbourhood in the social scale. We evaluate our proposed method against two\r\nbaseline methods and a state-of-art technique on three public datasets. The results show that our method outperforms\r\nother methods and that using circular shape neighbourhood\r\nimproves the prediction accuracy","url_abs":"https://ieeexplore.ieee.org/document/8354239","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8354239","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ss-lstm-a-hierarchical-lstm-model-for","repo_url":"https://github.com/xuehaouwa/SS-LSTM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"pedestrian-trajectory-prediction","task_name":"Pedestrian Trajectory Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}