{"id":"https://openalex.org/W2418081708","doi":"https://doi.org/10.1109/icra.2016.7487768","title":"GLMP- realtime pedestrian path prediction using global and local movement patterns","display_name":"GLMP- realtime pedestrian path prediction using global and local movement patterns","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2418081708","doi":"https://doi.org/10.1109/icra.2016.7487768","mag":"2418081708"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2016.7487768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058453308","display_name":"Aniket Bera","orcid":"https://orcid.org/0000-0002-0182-6985"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aniket Bera","raw_affiliation_strings":["Department of Computer Science, University of North Carolina at Chapel Hill, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of North Carolina at Chapel Hill, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100739942","display_name":"Sujeong Kim","orcid":"https://orcid.org/0000-0003-0471-8995"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sujeong Kim","raw_affiliation_strings":["Department of Computer Science, University of North Carolina at Chapel Hill, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of North Carolina at Chapel Hill, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005410534","display_name":"Tanmay Randhavane","orcid":"https://orcid.org/0000-0003-0509-706X"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tanmay Randhavane","raw_affiliation_strings":["Department of Computer Science, University of North Carolina at Chapel Hill, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of North Carolina at Chapel Hill, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013538184","display_name":"Srihari Pratapa","orcid":null},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Srihari Pratapa","raw_affiliation_strings":["Department of Computer Science, University of North Carolina at Chapel Hill, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of North Carolina at Chapel Hill, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004194238","display_name":"Dinesh Manocha","orcid":"https://orcid.org/0000-0001-7047-9801"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dinesh Manocha","raw_affiliation_strings":["Department of Computer Science, University of North Carolina at Chapel Hill, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of North Carolina at Chapel Hill, USA","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5058453308"],"corresponding_institution_ids":["https://openalex.org/I114027177"],"apc_list":null,"apc_paid":null,"fwci":5.1772,"has_fulltext":false,"cited_by_count":72,"citation_normalized_percentile":{"value":0.9715542,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5528","last_page":"5535"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/precomputation","display_name":"Precomputation","score":0.8954721689224243},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7746821641921997},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7069708108901978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6437731385231018},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6298688650131226},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5238590240478516},{"id":"https://openalex.org/keywords/movement","display_name":"Movement (music)","score":0.4789717197418213},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4751165509223938},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.47208234667778015},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46636614203453064},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4586603045463562},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2214677333831787},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14085429906845093}],"concepts":[{"id":"https://openalex.org/C159379195","wikidata":"https://www.wikidata.org/wiki/Q7239568","display_name":"Precomputation","level":3,"score":0.8954721689224243},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7746821641921997},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7069708108901978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6437731385231018},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6298688650131226},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5238590240478516},{"id":"https://openalex.org/C2780226923","wikidata":"https://www.wikidata.org/wiki/Q929848","display_name":"Movement (music)","level":2,"score":0.4789717197418213},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4751165509223938},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.47208234667778015},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46636614203453064},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4586603045463562},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2214677333831787},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14085429906845093},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C107038049","wikidata":"https://www.wikidata.org/wiki/Q35986","display_name":"Aesthetics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2016.7487768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306077","display_name":"Boeing","ror":"https://ror.org/04sm5zn07"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W192919555","https://openalex.org/W630739203","https://openalex.org/W1541596375","https://openalex.org/W1584874746","https://openalex.org/W1963649406","https://openalex.org/W1967777429","https://openalex.org/W1969622215","https://openalex.org/W1999839266","https://openalex.org/W2038621822","https://openalex.org/W2040819715","https://openalex.org/W2071005201","https://openalex.org/W2072956256","https://openalex.org/W2087622837","https://openalex.org/W2088604406","https://openalex.org/W2101668106","https://openalex.org/W2101821104","https://openalex.org/W2121215404","https://openalex.org/W2133552987","https://openalex.org/W2139479830","https://openalex.org/W2146183743","https://openalex.org/W2157304242","https://openalex.org/W2158955189","https://openalex.org/W2161686298","https://openalex.org/W2164489414","https://openalex.org/W2167052694","https://openalex.org/W2170193830","https://openalex.org/W2246660365","https://openalex.org/W2286744228","https://openalex.org/W2532516272","https://openalex.org/W3142298583","https://openalex.org/W6675188273","https://openalex.org/W6678073934","https://openalex.org/W6691152312"],"related_works":["https://openalex.org/W126258643","https://openalex.org/W2157769033","https://openalex.org/W2096978683","https://openalex.org/W414359703","https://openalex.org/W2687399516","https://openalex.org/W2170398238","https://openalex.org/W2543067032","https://openalex.org/W3029113864","https://openalex.org/W2020308516","https://openalex.org/W4242327143"],"abstract_inverted_index":{"We":[0,36],"present":[1],"a":[2],"novel":[3],"real-time":[4,84],"algorithm":[5,89],"to":[6,59],"predict":[7],"the":[8,39,60,83,114],"path":[9],"of":[10,41,86,106,116],"pedestrians":[11],"in":[12,124],"cluttered":[13],"environments.":[14],"Our":[15,75],"approach":[16,76],"makes":[17],"no":[18,78],"assumption":[19],"about":[20],"pedestrian":[21,42],"motion":[22,43],"or":[23],"crowd":[24,102],"density,":[25],"and":[26,44,62,65,72,80,92,100,108],"is":[27],"useful":[28],"for":[29],"short-term":[30],"as":[31,33,69],"well":[32],"long-term":[34,117],"prediction.":[35],"interactively":[37],"learn":[38],"characteristics":[40,67],"movement":[45,56,73,110],"patterns":[46,57,111],"from":[47,97],"2D":[48],"trajectories":[49],"using":[50],"Bayesian":[51],"inference.":[52],"These":[53],"include":[54],"local":[55,107],"corresponding":[58],"current":[61],"preferred":[63],"velocities":[64],"global":[66,109],"such":[68],"entry":[70],"points":[71],"features.":[74],"involves":[77],"precomputation":[79],"we":[81],"demonstrate":[82],"performance":[85],"our":[87],"prediction":[88,118],"on":[90],"sparse":[91],"noisy":[93],"trajectory":[94],"data":[95],"extracted":[96],"dense":[98],"indoor":[99],"outdoor":[101],"videos.":[103,126],"The":[104],"combination":[105],"can":[112],"improve":[113],"accuracy":[115],"by":[119],"12-18%":[120],"over":[121],"prior":[122],"methods":[123],"high-density":[125]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
