{"id":"https://openalex.org/W3010309142","doi":"https://doi.org/10.1109/wacv45572.2020.9093347","title":"Graph Networks for Multiple Object Tracking","display_name":"Graph Networks for Multiple Object Tracking","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3010309142","doi":"https://doi.org/10.1109/wacv45572.2020.9093347","mag":"3010309142"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093347","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093347","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5102013440","display_name":"Jiahe Li","orcid":"https://orcid.org/0009-0003-5092-1326"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiahe Li","raw_affiliation_strings":["NELVT, Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NELVT, Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100547696","display_name":"Xu Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Gao","raw_affiliation_strings":["NELVT, Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NELVT, Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101606698","display_name":"Tingting Jiang","orcid":"https://orcid.org/0000-0002-5372-0656"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Jiang","raw_affiliation_strings":["NELVT, Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NELVT, Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102013440"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":5.5905,"has_fulltext":false,"cited_by_count":92,"citation_normalized_percentile":{"value":0.96860251,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"708","last_page":"717"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9937000274658203,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.989799976348877,"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":0.6897298693656921},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6167018413543701},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.387453556060791},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3857632875442505}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6897298693656921},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6167018413543701},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.387453556060791},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3857632875442505}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093347","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093347","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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":60,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1522301498","https://openalex.org/W1528063097","https://openalex.org/W1531192956","https://openalex.org/W1908451628","https://openalex.org/W1976315892","https://openalex.org/W2016135469","https://openalex.org/W2035153336","https://openalex.org/W2053744956","https://openalex.org/W2111644456","https://openalex.org/W2127084114","https://openalex.org/W2168356304","https://openalex.org/W2171243491","https://openalex.org/W2194775991","https://openalex.org/W2225887246","https://openalex.org/W2237765446","https://openalex.org/W2260975248","https://openalex.org/W2291627510","https://openalex.org/W2474389331","https://openalex.org/W2520234541","https://openalex.org/W2534578893","https://openalex.org/W2547098537","https://openalex.org/W2552391307","https://openalex.org/W2579024533","https://openalex.org/W2739374836","https://openalex.org/W2739491435","https://openalex.org/W2794143688","https://openalex.org/W2805516822","https://openalex.org/W2883638665","https://openalex.org/W2886910176","https://openalex.org/W2894909756","https://openalex.org/W2895071559","https://openalex.org/W2895150009","https://openalex.org/W2897582990","https://openalex.org/W2897770249","https://openalex.org/W2900208617","https://openalex.org/W2900871370","https://openalex.org/W2901925479","https://openalex.org/W2914819564","https://openalex.org/W2918745114","https://openalex.org/W2921601546","https://openalex.org/W2945621340","https://openalex.org/W2952915411","https://openalex.org/W2962923976","https://openalex.org/W2963063317","https://openalex.org/W2963313370","https://openalex.org/W2963481014","https://openalex.org/W2963940252","https://openalex.org/W2964019074","https://openalex.org/W2964121744","https://openalex.org/W2996478685","https://openalex.org/W4295331127","https://openalex.org/W6631190155","https://openalex.org/W6696672603","https://openalex.org/W6703780166","https://openalex.org/W6729508183","https://openalex.org/W6751796012","https://openalex.org/W6753818355","https://openalex.org/W6756605840","https://openalex.org/W6759405438"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Multiple":[0],"object":[1],"tracking":[2],"(MOT)":[3],"task":[4],"requires":[5],"reasoning":[6],"the":[7,27,34,40,90,93,113,117,127,144,147,155,158,165],"states":[8],"of":[9,146,168],"all":[10],"targets":[11,15],"and":[12,32,83,92,112,157,161],"associating":[13],"these":[14,49,62],"in":[16,103,116],"a":[17,44,66,84,134],"global":[18,35,114,123,128],"way.":[19],"However,":[20,48],"existing":[21],"MOT":[22,41,69],"methods":[23,38,50],"mostly":[24],"focus":[25],"on":[26,53,153],"local":[28],"relationship":[29,129],"among":[30],"objects":[31],"ignore":[33],"relationship.":[36],"Some":[37],"formulate":[39],"problem":[42],"as":[43],"graph":[45,74,81,86,105,118],"optimization":[46],"problem.":[47],"are":[51,57],"based":[52],"static":[54],"graphs,":[55],"which":[56,107],"seldom":[58],"updated.":[59,121],"To":[60],"solve":[61],"problems,":[63],"we":[64,77],"design":[65,78],"new":[67],"near-online":[68],"method":[70,150],"with":[71],"an":[72,79],"end-to-end":[73],"network.":[75],"Specifically,":[76],"appearance":[80,91],"network":[82,87],"motion":[85,94],"to":[88,130,136,142],"capture":[89,126],"similarity":[95],"separately.":[96],"The":[97,122],"updating":[98],"mechanism":[99],"is":[100,140,151],"carefully":[101],"designed":[102],"our":[104,169],"network,":[106],"means":[108],"that":[109],"nodes,":[110],"edges":[111],"variable":[115,124],"can":[119,125],"be":[120],"help":[131],"tracking.":[132],"Finally,":[133],"strategy":[135],"handle":[137],"missing":[138],"detections":[139],"proposed":[141],"remedy":[143],"defect":[145],"detectors.":[148],"Our":[149],"evaluated":[152],"both":[154],"MOT16":[156],"MOT17":[159],"benchmarks,":[160],"experimental":[162],"results":[163],"show":[164],"encouraging":[166],"performance":[167],"method.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-05-16T08:24:45.110214","created_date":"2025-10-10T00:00:00"}
