{"id":"https://openalex.org/W3091076750","doi":"https://doi.org/10.1109/icip40778.2020.9190802","title":"How Incompletely Segmented Information Affects Multi-Object Tracking and Segmentation (MOTS)","display_name":"How Incompletely Segmented Information Affects Multi-Object Tracking and Segmentation (MOTS)","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3091076750","doi":"https://doi.org/10.1109/icip40778.2020.9190802","mag":"3091076750"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9190802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190802","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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/A5036243410","display_name":"Yu-Sheng Chou","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Sheng Chou","raw_affiliation_strings":["Graduate Institute of Networking and Multimedia, National Taiwan University, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate Institute of Networking and Multimedia, National Taiwan University, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072470661","display_name":"Chien-Yao Wang","orcid":"https://orcid.org/0000-0002-2946-8972"},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chien-Yao Wang","raw_affiliation_strings":["Academia Sinica, Institute of Information Science, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academia Sinica, Institute of Information Science, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087480257","display_name":"Shou-De Lin","orcid":"https://orcid.org/0000-0001-9970-1250"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shou-De Lin","raw_affiliation_strings":["Graduate Institute of Networking and Multimedia, National Taiwan University, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate Institute of Networking and Multimedia, National Taiwan University, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108538229","display_name":"Hong-Yuan Mark Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Hong-Yuan Mark Liao","raw_affiliation_strings":["Academia Sinica, Institute of Information Science, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academia Sinica, Institute of Information Science, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0979,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.4129075,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2086","last_page":"2090"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9957000017166138,"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":0.9940000176429749,"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":0.8232824802398682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7843014597892761},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7822641134262085},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.7111964821815491},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.610709011554718},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5804967284202576},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5556598901748657},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5530550479888916},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5119537115097046},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.473722368478775},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.44339093565940857},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4255371689796448},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39590463042259216},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.24055224657058716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8232824802398682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7843014597892761},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7822641134262085},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.7111964821815491},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.610709011554718},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5804967284202576},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5556598901748657},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5530550479888916},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5119537115097046},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.473722368478775},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44339093565940857},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4255371689796448},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39590463042259216},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24055224657058716},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip40778.2020.9190802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190802","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2103846358","https://openalex.org/W2150066425","https://openalex.org/W2194775991","https://openalex.org/W2252355370","https://openalex.org/W2598634450","https://openalex.org/W2603203130","https://openalex.org/W2613718673","https://openalex.org/W2791709739","https://openalex.org/W2884030607","https://openalex.org/W2889935068","https://openalex.org/W2920942303","https://openalex.org/W2934488709","https://openalex.org/W2950703532","https://openalex.org/W2953920664","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963227409","https://openalex.org/W2963341924","https://openalex.org/W2963420686","https://openalex.org/W2963857746","https://openalex.org/W2981393651","https://openalex.org/W2998228095","https://openalex.org/W3098744844","https://openalex.org/W3099887740","https://openalex.org/W3104218139","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6717697761","https://openalex.org/W6735531217","https://openalex.org/W6743731764","https://openalex.org/W6761295145","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4327500857","https://openalex.org/W4311223090","https://openalex.org/W1689909837","https://openalex.org/W2965994363","https://openalex.org/W4205729548","https://openalex.org/W1895541646","https://openalex.org/W4301521271","https://openalex.org/W2889698616","https://openalex.org/W2953362004","https://openalex.org/W2901505109"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"deep":[3,39],"learning":[4,132],"has":[5],"made":[6],"dramatic":[7],"advances":[8],"in":[9,14,37,78,170],"computer":[10],"vision":[11],"field,":[12],"especially":[13],"improving":[15],"the":[16,53,83,98,116,155,166,183,186],"performance":[17,66,84,147],"of":[18,85,100,145,185],"object":[19,41],"detection":[20],"as":[21,23,73],"well":[22],"instance":[24,121],"semantic":[25,122],"segmentation.":[26],"Still,":[27],"multi-object":[28,108],"tracking":[29,54,65,109],"(MOT)":[30],"remains":[31],"a":[32,43,79,128],"very":[33],"challenging":[34],"issue.":[35],"Even":[36],"state-of-the-art":[38,87],"learning-based":[40],"detectors,":[42],"preferred":[44],"paradigm":[45],"for":[46,64,89],"MOT:":[47],"tracking-by-detection,":[48],"can":[49,136],"only":[50],"slightly":[51],"improve":[52],"performance.":[55],"Pixel-level":[56],"information":[57,93,117],"is":[58,94,113,124],"considered":[59],"more":[60],"precise":[61],"and":[62,110,162,179],"useful":[63],"improvement":[67],"than":[68],"using":[69],"conventional":[70],"information,":[71],"such":[72],"foreground":[74],"or":[75],"background":[76],"content":[77],"bounding":[80],"box.":[81],"However,":[82],"current":[86],"models":[88],"automatically":[90],"annotating":[91],"pixel-level":[92],"still":[95],"far":[96],"from":[97],"expectation":[99],"human":[101],"beings.":[102],"Therefore,":[103],"we":[104],"shall":[105],"explore":[106],"how":[107],"segmentation":[111,123],"(MOTS)":[112],"affected":[114],"when":[115,148],"obtained":[118],"after":[119],"applying":[120],"incomplete.":[125],"We":[126,153],"propose":[127],"mask-guided":[129],"two-streamed":[130],"augmentation":[131],"(MGTSAL)":[133],"algorithm,":[134],"which":[135],"be":[137],"applied":[138],"to":[139,141],"TrackR-CNN":[140,169],"alleviate":[142],"significant":[143],"drop":[144],"MOTS":[146,159],"encountering":[149],"incompletely":[150],"segmented":[151],"information.":[152],"evaluate":[154],"proposed":[156,187],"approach":[157,164],"on":[158],"KITTI":[160],"dataset,":[161],"our":[163,172],"outperforms":[165],"baseline":[167],"model":[168],"all":[171],"experimental":[173,177],"settings.":[174],"The":[175],"promising":[176],"results":[178],"ablation":[180],"study":[181],"validate":[182],"effectiveness":[184],"approach.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
