{"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/social-attention-modeling-attention-in-human","title":"Social Attention: Modeling Attention in Human Crowds","arxiv_id":"1710.04689","date":"2017-10-12","proceeding":null,"authors":["Anirudh Vemula","Katharina Muelling","Jean Oh"],"abstract":"Robots that navigate through human crowds need to be able to plan safe,\nefficient, and human predictable trajectories. This is a particularly\nchallenging problem as it requires the robot to predict future human\ntrajectories within a crowd where everyone implicitly cooperates with each\nother to avoid collisions. Previous approaches to human trajectory prediction\nhave modeled the interactions between humans as a function of proximity.\nHowever, that is not necessarily true as some people in our immediate vicinity\nmoving in the same direction might not be as important as other people that are\nfurther away, but that might collide with us in the future. In this work, we\npropose Social Attention, a novel trajectory prediction model that captures the\nrelative importance of each person when navigating in the crowd, irrespective\nof their proximity. We demonstrate the performance of our method against a\nstate-of-the-art approach on two publicly available crowd datasets and analyze\nthe trained attention model to gain a better understanding of which surrounding\nagents humans attend to, when navigating in a crowd.","url_abs":"http://arxiv.org/abs/1710.04689v2","url_pdf":"http://arxiv.org/pdf/1710.04689v2.pdf","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":"social-attention-modeling-attention-in-human","repo_url":"https://github.com/cmubig/socialAttention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"social-attention-modeling-attention-in-human","repo_url":"https://github.com/huang-xx/STGAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.04689","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.04689"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cmubig/socialAttention","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/huang-xx/STGAT","reach":{"status":"ok"}}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"55df9dc702dcf13c","entry":"evaluate_helper","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"55df9dc702dcf13c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}