{"id":"https://openalex.org/W4413945301","doi":"https://doi.org/10.1109/icra55743.2025.11128508","title":"Learning Better Representations for Crowded Pedestrians in Offboard LiDAR-Camera 3D Tracking-by-detection","display_name":"Learning Better Representations for Crowded Pedestrians in Offboard LiDAR-Camera 3D Tracking-by-detection","publication_year":2025,"publication_date":"2025-05-19","ids":{"openalex":"https://openalex.org/W4413945301","doi":"https://doi.org/10.1109/icra55743.2025.11128508"},"language":"en","primary_location":{"id":"doi:10.1109/icra55743.2025.11128508","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra55743.2025.11128508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5100615081","display_name":"Shichao Li","orcid":"https://orcid.org/0000-0001-6858-5031"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shichao Li","raw_affiliation_strings":["Zhuoyu Technology,Department of Perception,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Zhuoyu Technology,Department of Perception,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101513272","display_name":"Peiliang Li","orcid":"https://orcid.org/0000-0001-7114-8107"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiliang Li","raw_affiliation_strings":["Zhuoyu Technology,Department of Perception,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Zhuoyu Technology,Department of Perception,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103919571","display_name":"Qing Lian","orcid":null},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Lian","raw_affiliation_strings":["Zhuoyu Technology,Department of Perception,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Zhuoyu Technology,Department of Perception,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103015145","display_name":"Yun Peng","orcid":"https://orcid.org/0000-0002-7592-2857"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Yun","raw_affiliation_strings":["Zhuoyu Technology,Department of Perception,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Zhuoyu Technology,Department of Perception,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089828993","display_name":"Xiaozhi Chen","orcid":"https://orcid.org/0009-0009-3064-6201"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaozhi Chen","raw_affiliation_strings":["Zhuoyu Technology,Department of Perception,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Zhuoyu Technology,Department of Perception,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100615081"],"corresponding_institution_ids":["https://openalex.org/I4210152380"],"apc_list":null,"apc_paid":null,"fwci":1.319,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8520956,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2740","last_page":"2747"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991000294685364,"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":0.9991000294685364,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9922999739646912,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9596999883651733,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/lidar","display_name":"Lidar","score":0.7773920893669128},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7011442184448242},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6744115948677063},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6236233711242676},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5958495140075684},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5332885384559631},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4349208474159241},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.4069625735282898},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3403282165527344},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.2513202428817749},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.24395540356636047},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1490696370601654},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.07330650091171265}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7773920893669128},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7011442184448242},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6744115948677063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6236233711242676},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5958495140075684},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5332885384559631},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4349208474159241},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.4069625735282898},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3403282165527344},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2513202428817749},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.24395540356636047},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1490696370601654},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.07330650091171265},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra55743.2025.11128508","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra55743.2025.11128508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W2096349671","https://openalex.org/W2411443472","https://openalex.org/W2566899507","https://openalex.org/W2883363148","https://openalex.org/W2897529137","https://openalex.org/W2962850098","https://openalex.org/W2962954622","https://openalex.org/W2963351448","https://openalex.org/W2967740791","https://openalex.org/W2982358316","https://openalex.org/W2990075400","https://openalex.org/W3001652308","https://openalex.org/W3014641072","https://openalex.org/W3034638324","https://openalex.org/W3034739212","https://openalex.org/W3034955056","https://openalex.org/W3035172746","https://openalex.org/W3035323039","https://openalex.org/W3035574168","https://openalex.org/W3095753995","https://openalex.org/W3096609285","https://openalex.org/W3109395584","https://openalex.org/W3132607695","https://openalex.org/W3138516171","https://openalex.org/W3166543103","https://openalex.org/W3167095230","https://openalex.org/W3196204376","https://openalex.org/W3207707490","https://openalex.org/W4214613509","https://openalex.org/W4214624153","https://openalex.org/W4312473433","https://openalex.org/W4312743914","https://openalex.org/W4312982704","https://openalex.org/W4382464460","https://openalex.org/W4383066393","https://openalex.org/W4386076253","https://openalex.org/W4386076400","https://openalex.org/W4390871916","https://openalex.org/W4390872451","https://openalex.org/W4390872843","https://openalex.org/W4390873008"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581","https://openalex.org/W2556125083"],"abstract_inverted_index":{"Perceiving":[0],"pedestrians":[1,71],"in":[2],"highly":[3,69],"crowded":[4,70,99],"urban":[5],"environments":[6],"is":[7,26],"a":[8,43,49,61],"difficult":[9],"long-tail":[10],"problem":[11],"for":[12,22,48,72,98,104],"learning-based":[13],"autonomous":[14],"perception.":[15],"Speeding":[16],"up":[17],"3D":[18,65,127],"ground":[19],"truth":[20],"generation":[21],"such":[23],"challenging":[24],"scenes":[25,100],"performance-critical":[27],"yet":[28],"very":[29],"challenging.":[30],"The":[31,135],"difficulties":[32],"include":[33],"the":[34,37,56,95,102,126],"sparsity":[35],"of":[36,45,68],"captured":[38],"pedestrian":[39,84,128],"point":[40,88],"cloud":[41,89],"and":[42,90,101,116],"lack":[44],"suitable":[46],"benchmarks":[47],"specific":[50],"system":[51,81],"design":[52],"study.":[53],"To":[54,93],"tackle":[55],"challenges,":[57],"we":[58,107],"first":[59],"collect":[60],"new":[62],"multi-view":[63,91],"LiDAR-camera":[64],"multiple-object-tracking":[66],"benchmark":[67],"in-depth":[73],"analysis.":[74],"We":[75],"then":[76],"build":[77],"an":[78],"offboard":[79],"auto-labeling":[80,133],"that":[82,113,121],"reconstructs":[83],"trajectories":[85],"from":[86],"LiDAR":[87],"images.":[92],"improve":[94],"generalization":[96],"power":[97],"performance":[103,130],"small":[105],"objects,":[106],"propose":[108],"to":[109],"learn":[110],"high-resolution":[111],"representations":[112],"are":[114],"density-aware":[115],"relationship-aware.":[117],"Extensive":[118],"experiments":[119],"validate":[120],"our":[122],"approach":[123],"significantly":[124],"improves":[125],"tracking":[129],"towards":[131],"higher":[132],"efficiency.":[134],"code":[136],"will":[137],"be":[138],"publicly":[139],"available":[140],"at":[141],"this":[142],"HTTP":[143],"URL<sup":[144],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[145,147],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup><sup":[146],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>https://github.com/Nicholasli1995/PCP-MV.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
