{"id":"https://openalex.org/W7138026746","doi":"https://doi.org/10.1609/aaai.v40i5.37406","title":"OmniPT: Unleashing the Potential of Large Vision Language Models for Pedestrian Tracking and Understanding","display_name":"OmniPT: Unleashing the Potential of Large Vision Language Models for Pedestrian Tracking and Understanding","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138026746","doi":"https://doi.org/10.1609/aaai.v40i5.37406"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i5.37406","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i5.37406","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37406/41368","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37406/41368","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129729031","display_name":"Teng Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Teng Fu","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129753183","display_name":"Mengyang Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengyang Zhao","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129647542","display_name":"Ke Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Niu","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129680724","display_name":"Kaixin Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaixin Peng","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129690513","display_name":"Bin Li","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Li","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5129729031"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3119403,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"5","first_page":"4031","last_page":"4039"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.7612000107765198,"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":0.7612000107765198,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.13030000030994415,"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":0.022199999541044235,"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/pedestrian","display_name":"Pedestrian","score":0.6963000297546387},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6536999940872192},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6488000154495239},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5737000107765198},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5392000079154968},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.53329998254776},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.42170000076293945},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4205999970436096},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4203000068664551}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8004999756813049},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6963000297546387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.675000011920929},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6536999940872192},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6488000154495239},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5737000107765198},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5392000079154968},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.53329998254776},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.517799973487854},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.42170000076293945},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4205999970436096},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4203000068664551},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.414900004863739},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.34860000014305115},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3416999876499176},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.328900009393692},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.2865000069141388},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.27219998836517334},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i5.37406","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i5.37406","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37406/41368","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i5.37406","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i5.37406","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37406/41368","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138026746.pdf","grobid_xml":"https://content.openalex.org/works/W7138026746.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"LVLMs":[0,27,89],"have":[1,46],"been":[2,47],"shown":[3],"to":[4,33,124,139,173,177,215,225],"perform":[5,168,200,244],"excellently":[6],"in":[7,16,53],"image-level":[8],"tasks":[9,71],"such":[10,20,60],"as":[11,21,61],"VQA":[12],"and":[13,24,57,67,111,137,180,208,221,234],"caption.":[14],"However,":[15],"many":[17],"instance-level":[18],"tasks,":[19],"visual":[22],"grounding":[23],"object":[25,55,79],"detection,":[26],"still":[28],"show":[29],"performance":[30,220],"gaps":[31],"compared":[32],"previous":[34,248],"expert":[35],"models.":[36],"Meanwhile,":[37],"although":[38],"pedestrian":[39,205],"tracking":[40,56,127,206,219,232],"is":[41,86],"a":[42,48,96,130,151,169,188,192],"classical":[43],"task,":[44],"there":[45],"number":[49,194],"of":[50,115,155,163,195],"new":[51,97],"topics":[52],"combining":[54],"natural":[58],"language,":[59],"Referring":[62,65],"MOT,":[63,66],"Cross-view":[64],"Semantic":[68],"MOT.":[69],"These":[70],"emphasize":[72],"that":[73,132,239],"models":[74,134],"should":[75],"understand":[76],"the":[77,126,141,160,164,175,217,235,240,247],"tracked":[78,116],"at":[80],"an":[81],"advanced":[82],"semantic":[83,113],"level,":[84],"which":[85,104],"exactly":[87],"where":[88],"excel.":[90],"In":[91],"this":[92,147],"paper,":[93],"we":[94,149,166,186,199],"propose":[95],"unified":[98],"Pedestrian":[99],"Tracking":[100],"framework,":[101],"namely":[102],"OmniPT,":[103],"can":[105,135,243],"track,":[106],"track":[107],"based":[108],"on":[109,159,203,231],"reference":[110],"generate":[112],"understanding":[114],"objects":[117],"interactively.":[118],"We":[119,228],"address":[120],"two":[121],"issues:":[122],"how":[123,138],"model":[125,142,176],"task":[128,131],"into":[129],"foundation":[133],"perform,":[136],"make":[140],"output":[143,178],"formatted":[144],"answers.":[145],"To":[146],"end,":[148],"implement":[150],"training":[152],"phase":[153,172,190,214],"consisting":[154],"RL-Mid":[156],"Training-SFT-RL.":[157],"Based":[158],"pre-trained":[161],"weights":[162],"LVLM,":[165],"first":[167],"simple":[170],"RL":[171,213],"enable":[174],"fixed":[179],"supervisable":[181],"bounding":[182],"box":[183],"format.":[184],"Subsequently,":[185],"conduct":[187,229],"mid-training":[189],"using":[191],"large":[193],"pedestrian-related":[196],"datasets.":[197],"Finally,":[198],"supervised":[201],"fine-tuning":[202],"several":[204],"datasets,":[207],"then":[209],"carry":[210],"out":[211],"another":[212],"improve":[216],"model's":[218],"enhance":[222],"its":[223],"ability":[224],"follow":[226],"instructions.":[227],"experiments":[230],"benchmarks":[233],"experimental":[236],"results":[237],"demonstrate":[238],"proposed":[241],"method":[242],"better":[245],"than":[246],"methods.":[249]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-03-18T00:00:00"}