{"id":"https://openalex.org/W3093256682","doi":"https://doi.org/10.1145/3394171.3416282","title":"Transductive Multi-Object Tracking in Complex Events by Interactive Self-Training","display_name":"Transductive Multi-Object Tracking in Complex Events by Interactive Self-Training","publication_year":2020,"publication_date":"2020-10-12","ids":{"openalex":"https://openalex.org/W3093256682","doi":"https://doi.org/10.1145/3394171.3416282","mag":"3093256682"},"language":"en","primary_location":{"id":"doi:10.1145/3394171.3416282","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3416282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5115590045","display_name":"Ancong Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ancong Wu","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026298162","display_name":"Chengzhi Lin","orcid":"https://orcid.org/0000-0002-8049-3989"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengzhi Lin","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055864867","display_name":"Bogao Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bogao Chen","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101885552","display_name":"Weihao Huang","orcid":"https://orcid.org/0000-0003-3459-7610"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihao Huang","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101982557","display_name":"Zeyu Huang","orcid":"https://orcid.org/0000-0002-8117-3231"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeyu Huang","raw_affiliation_strings":["ACCUVISION Technology Co., Ltd., Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ACCUVISION Technology Co., Ltd., Guangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108050904","display_name":"Wei\u2010Shi Zheng","orcid":"https://orcid.org/0000-0001-8327-0003"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei-Shi Zheng","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4620","last_page":"4624"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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":0.9972000122070312,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/computer-science","display_name":"Computer science","score":0.8310778141021729},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7456894516944885},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.721904993057251},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5668728947639465},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5097188353538513},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4986572265625},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.49726608395576477},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.493312269449234},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.4617839455604553},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.37975120544433594},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.11464139819145203}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8310778141021729},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7456894516944885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.721904993057251},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5668728947639465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5097188353538513},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4986572265625},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.49726608395576477},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.493312269449234},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4617839455604553},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.37975120544433594},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.11464139819145203},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394171.3416282","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3416282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W639708223","https://openalex.org/W1521019969","https://openalex.org/W1995945562","https://openalex.org/W2107775979","https://openalex.org/W2252355370","https://openalex.org/W2291627510","https://openalex.org/W2603203130","https://openalex.org/W2766984662","https://openalex.org/W2798542761","https://openalex.org/W2806070179","https://openalex.org/W2886581236","https://openalex.org/W2953920664","https://openalex.org/W2963047834","https://openalex.org/W2967515867","https://openalex.org/W3012922853","https://openalex.org/W3104218139","https://openalex.org/W3106889297","https://openalex.org/W3175630421","https://openalex.org/W4297683907"],"related_works":["https://openalex.org/W4297577100","https://openalex.org/W4389474468","https://openalex.org/W4300172004","https://openalex.org/W4321649381","https://openalex.org/W2997645659","https://openalex.org/W3180787869","https://openalex.org/W3203792196","https://openalex.org/W2955455867","https://openalex.org/W4295929828","https://openalex.org/W3156096827"],"abstract_inverted_index":{"Recently,":[0],"multi-object":[1],"tracking":[2,98],"(MOT)":[3],"for":[4,55,84],"estimating":[5],"trajectories":[6],"of":[7,110,134,166,176],"pedestrians":[8],"has":[9],"undergone":[10],"fast":[11],"development":[12],"and":[13,46,127,171],"played":[14],"an":[15,119],"important":[16],"role":[17],"in":[18,25,30,42,71,118,180,186,197],"human-centric":[19],"video":[20,23],"analysis.":[21],"However,":[22],"analysis":[24],"complex":[26,37],"events":[27],"(e.g.":[28],"scenes":[29,45,103],"HiEve":[31,169],"dataset)":[32],"is":[33,82],"still":[34],"under-explored.":[35],"In":[36],"real-world":[38],"scenarios,":[39],"domain":[40,61,65],"gap":[41],"unseen":[43,101,120],"testing":[44,80,106],"severe":[47],"occlusion":[48],"problem":[49,70],"that":[50,78],"disconnects":[51],"tracks":[52],"are":[53],"challenging":[54],"existing":[56],"online":[57],"MOT":[58],"methods":[59],"without":[60],"adaptation.":[62],"To":[63,114,143],"alleviate":[64],"gap,":[66],"we":[67,122,150],"study":[68],"the":[69,97,139,158,173,187],"a":[72,90],"transductive":[73,91],"learning":[74,85],"setting,":[75],"which":[76],"assumes":[77],"unlabeled":[79,105,135],"data":[81,107,136],"available":[83],"offline":[86],"tracking.":[87],"We":[88],"propose":[89],"interactive":[92],"self-training":[93],"method":[94,163],"to":[95,100,156],"adapt":[96],"model":[99,130,141],"crowded":[102],"with":[104,131],"by":[108,138],"means":[109],"teacher-student":[111],"interative":[112],"learning.":[113],"reduce":[115],"prediction":[116],"variance":[117],"domain,":[121],"train":[123],"two":[124],"different":[125],"models":[126],"teach":[128],"one":[129],"pseudo":[132,160],"labels":[133],"predicted":[137,159],"other":[140],"interactively.":[142],"improve":[144],"robustness":[145],"against":[146],"occlusions":[147],"during":[148],"self-training,":[149],"exploit":[151],"disconnected":[152],"track":[153],"interpolation":[154],"(DTI)":[155],"refine":[157],"labels.":[161],"Our":[162],"achieved":[164],"MOTA":[165],"60.23":[167],"on":[168,192],"dataset":[170],"won":[172],"first":[174],"place":[175],"Multi-person":[177],"Motion":[178],"Tracking":[179],"Complex":[181,198],"Events":[182],"(with":[183],"Private":[184],"Detection)":[185],"ACM":[188],"MM":[189],"Grand":[190],"Challenge":[191],"Large-scale":[193],"Human-centric":[194],"Video":[195],"Analysis":[196],"Events.":[199]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
