{"id":"https://openalex.org/W4383097409","doi":"https://doi.org/10.1109/icra48891.2023.10160273","title":"Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints","display_name":"Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints","publication_year":2023,"publication_date":"2023-05-29","ids":{"openalex":"https://openalex.org/W4383097409","doi":"https://doi.org/10.1109/icra48891.2023.10160273"},"language":"en","primary_location":{"id":"doi:10.1109/icra48891.2023.10160273","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48891.2023.10160273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5100357070","display_name":"Jiachen Li","orcid":"https://orcid.org/0000-0002-4883-697X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiachen Li","raw_affiliation_strings":["Stanford University"],"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102949974","display_name":"Xinwei Shi","orcid":"https://orcid.org/0009-0001-1150-6686"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinwei Shi","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100643586","display_name":"Feiyu Chen","orcid":"https://orcid.org/0000-0002-9408-4393"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feiyu Chen","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078538980","display_name":"Jonathan Stroud","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jonathan Stroud","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102732183","display_name":"Zhishuai Zhang","orcid":"https://orcid.org/0009-0006-0383-4811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhishuai Zhang","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018464968","display_name":"Tian Lan","orcid":"https://orcid.org/0000-0003-3010-8090"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian Lan","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086636630","display_name":"Junhua Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junhua Mao","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101209625","display_name":"Jeonhyung Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeonhyung Kang","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031024662","display_name":"Khaled S. Refaat","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khaled S. Refaat","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101699552","display_name":"Weilong Yang","orcid":"https://orcid.org/0000-0001-7888-2304"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weilong Yang","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013936779","display_name":"Eugene Ie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eugene Ie","raw_affiliation_strings":["Waymo"],"affiliations":[{"raw_affiliation_string":"Waymo","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100332246","display_name":"Congcong Li","orcid":"https://orcid.org/0000-0003-4633-4391"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Congcong Li","raw_affiliation_strings":["Stanford University"],"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":12,"corresponding_author_ids":["https://openalex.org/A5100357070"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":2.1046,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.86716107,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1463","last_page":"1470"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9977999925613403,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9965999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8239044547080994},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6922690868377686},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6776875257492065},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.662325918674469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6478379964828491},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5413591265678406},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5384777188301086},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5053444504737854},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.48904284834861755},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4628550112247467},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4481010138988495},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.4451081156730652},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4296254515647888},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37395864725112915},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08062013983726501}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8239044547080994},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6922690868377686},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6776875257492065},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.662325918674469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6478379964828491},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5413591265678406},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5384777188301086},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5053444504737854},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.48904284834861755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4628550112247467},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4481010138988495},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.4451081156730652},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4296254515647888},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37395864725112915},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08062013983726501},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra48891.2023.10160273","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48891.2023.10160273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W2321533354","https://openalex.org/W2424778531","https://openalex.org/W2958360136","https://openalex.org/W2962730651","https://openalex.org/W2963065614","https://openalex.org/W2963076818","https://openalex.org/W2963158576","https://openalex.org/W2963548793","https://openalex.org/W2963669520","https://openalex.org/W2963697717","https://openalex.org/W2964203186","https://openalex.org/W2982573856","https://openalex.org/W2991484432","https://openalex.org/W3003906095","https://openalex.org/W3005680577","https://openalex.org/W3008700642","https://openalex.org/W3023371261","https://openalex.org/W3028769608","https://openalex.org/W3034722190","https://openalex.org/W3034781633","https://openalex.org/W3035096461","https://openalex.org/W3035172746","https://openalex.org/W3035545045","https://openalex.org/W3035725276","https://openalex.org/W3045246204","https://openalex.org/W3062588417","https://openalex.org/W3090783937","https://openalex.org/W3108908812","https://openalex.org/W3109717189","https://openalex.org/W3114632476","https://openalex.org/W3116651890","https://openalex.org/W3173252859","https://openalex.org/W3190699669","https://openalex.org/W3204431142","https://openalex.org/W3205492849","https://openalex.org/W3208797976","https://openalex.org/W3209693651","https://openalex.org/W4226016257","https://openalex.org/W4287778673","https://openalex.org/W4287812705","https://openalex.org/W4312587200","https://openalex.org/W4318963850","https://openalex.org/W6747795875","https://openalex.org/W6758354414","https://openalex.org/W6760206936","https://openalex.org/W6769043036","https://openalex.org/W6774314701","https://openalex.org/W6776598532","https://openalex.org/W6776700526","https://openalex.org/W6779332932","https://openalex.org/W6779371077","https://openalex.org/W6782468546","https://openalex.org/W6784071224","https://openalex.org/W6794633316"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W2197846993","https://openalex.org/W1976827262","https://openalex.org/W49697837","https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W3181401746","https://openalex.org/W2982423860"],"abstract_inverted_index":{"Accurate":[0],"understanding":[1],"and":[2,17,36,55,69,93,113,123,160],"prediction":[3],"of":[4,52,64,141,172,177],"human":[5,66,76,99,111],"behaviors":[6],"are":[7],"critical":[8],"prerequisites":[9],"for":[10,88],"autonomous":[11],"vehicles,":[12],"especially":[13],"in":[14,23,183],"highly":[15],"dynamic":[16],"interactive":[18],"scenarios":[19],"such":[20],"as":[21,153,155],"intersections":[22],"dense":[24],"urban":[25],"areas.":[26],"In":[27,78],"this":[28,79],"work,":[29],"we":[30,45,81,116],"aim":[31],"at":[32],"identifying":[33],"crossing":[34,90],"pedestrians":[35],"predicting":[37],"their":[38],"future":[39],"trajectories.":[40],"To":[41],"achieve":[42],"these":[43],"goals,":[44],"not":[46],"only":[47],"need":[48,61],"the":[49,65,132,139],"context":[50],"information":[51,63,109],"road":[53],"geometry":[54],"other":[56],"traffic":[57],"participants":[58],"but":[59],"also":[60],"fine-grained":[62],"pose,":[67],"motion":[68],"activity,":[70],"which":[71,96,136],"can":[72],"be":[73],"inferred":[74],"from":[75,102],"keypoints.":[77],"paper,":[80],"propose":[82,117],"a":[83,149,156,169,184],"novel":[84],"multi-task":[85],"learning":[86,125],"framework":[87],"pedestrian":[89],"action":[91],"recognition":[92],"trajectory":[94],"pre-diction,":[95],"utilizes":[97],"3D":[98],"keypoints":[100,134],"extracted":[101],"raw":[103],"sensor":[104],"data":[105],"to":[106,118,126,130],"capture":[107],"rich":[108],"on":[110,148,168],"pose":[112],"activity.":[114],"Moreover,":[115],"apply":[119],"two":[120],"auxiliary":[121,128],"tasks":[122],"contrastive":[124],"enable":[127],"supervisions":[129],"improve":[131],"learned":[133],"representation,":[135],"further":[137],"enhances":[138],"performance":[140,167],"major":[142],"tasks.":[143],"We":[144],"validate":[145],"our":[146,163],"approach":[147,164],"large-scale":[150],"in-house":[151],"dataset,":[152,159],"well":[154],"public":[157],"benchmark":[158],"show":[161],"that":[162],"achieves":[165],"state-of-the-art":[166],"wide":[170],"range":[171],"evaluation":[173],"metrics.":[174],"The":[175],"effectiveness":[176],"each":[178],"model":[179],"component":[180],"is":[181],"validated":[182],"detailed":[185],"ablation":[186],"study.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":8}],"updated_date":"2026-04-06T07:47:59.780226","created_date":"2025-10-10T00:00:00"}