{"id":"https://openalex.org/W6945110257","doi":"https://doi.org/10.21227/5rrc-se98","title":"MARS-Gait","display_name":"MARS-Gait","publication_year":2024,"publication_date":"2024-04-24","ids":{"openalex":"https://openalex.org/W6945110257","doi":"https://doi.org/10.21227/5rrc-se98"},"language":"en","primary_location":{"id":"doi:10.21227/5rrc-se98","is_oa":true,"landing_page_url":"https://doi.org/10.21227/5rrc-se98","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/5rrc-se98","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wang, Likai","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang, Likai","raw_affiliation_strings":["Tianjin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Xiangqun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiangqun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Han, Ruize","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Ruize","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Feng, Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Wang, Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Song","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"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":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.0828000009059906,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.0828000009059906,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13568","display_name":"Wood and Agarwood Research","score":0.07259999960660934,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13238","display_name":"Botanical Studies and Applications","score":0.028200000524520874,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.5656999945640564},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5372999906539917},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5019000172615051},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4050999879837036},{"id":"https://openalex.org/keywords/gait-analysis","display_name":"Gait analysis","score":0.3750999867916107},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33079999685287476}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7483999729156494},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6837999820709229},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.5656999945640564},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5372999906539917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5260999798774719},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5019000172615051},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C173906292","wikidata":"https://www.wikidata.org/wiki/Q1493441","display_name":"Gait analysis","level":3,"score":0.3750999867916107},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C77265313","wikidata":"https://www.wikidata.org/wiki/Q879844","display_name":"Rest (music)","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C83260615","wikidata":"https://www.wikidata.org/wiki/Q6773121","display_name":"Mars Exploration Program","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C2985215115","wikidata":"https://www.wikidata.org/wiki/Q22305006","display_name":"Gait cycle","level":3,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/5rrc-se98","is_oa":true,"landing_page_url":"https://doi.org/10.21227/5rrc-se98","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/5rrc-se98","is_oa":true,"landing_page_url":"https://doi.org/10.21227/5rrc-se98","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"MARS-Gait":[0,127],"is":[1,8,31,117],"a":[2],"new":[3],"outdoor":[4],"gait":[5,67,109,134],"recognition":[6,68],"dataset. It":[7],"built":[9],"based":[10],"on":[11],"the":[12,25,46,50,54,62,66,76,107,122,148],"MARS":[13,30],"(Motion":[14],"Analysis":[15],"and":[16,58,105,132,147],"Re-identification":[17],"Set)":[18],"dataset,":[19,69],"whose":[20],"samples":[21],"are":[22,80,143],"collected":[23],"in":[24,45],"campus":[26,47],"of":[27,114,121,129,136],"Tsinghua":[28],"university.":[29],"used":[32,144],"for":[33,48,145,155],"video-based":[34],"person":[35,57,112],"Re-ID":[36],"task,":[37],"which":[38,137],"uses":[39],"6":[40],"near-":[41],"synchronized":[42],"cameras":[43],"located":[44],"recording":[49],"pedestrians.":[51],"It":[52],"provides":[53],"tracklets":[55,77],"with":[56,140,152],"camera":[59],"IDs":[60],"as":[61,85],"annotations.":[63],"To":[64],"obtain":[65],"we":[70,91],"first":[71],"screened":[72],"all":[73],"samples,":[74],"removing":[75],"where":[78],"people":[79],"obviously":[81],"not":[82],"walking,":[83],"such":[84],"cycling":[86],"or":[87],"sitting.":[88],"After":[89],"that,":[90],"adopt":[92],"an":[93],"instance":[94],"segmentation":[95],"algorithm":[96],"HTC,":[97],"to":[98],"extract":[99],"silhouettes":[100],"from":[101,119],"original":[102,123],"RGB":[103],"sequences":[104,142,154],"get":[106],"corresponding":[108],"sequences.":[110],"The":[111],"ID":[113],"each":[115],"sample":[116],"inherited":[118],"that":[120],"tracklet.":[124],"In":[125],"total,":[126],"consists":[128],"1,246":[130],"subjects":[131,139,151],"16,093":[133],"sequences,":[135],"616":[138],"8,101":[141],"training":[146],"rest":[149],"630":[150],"7,992":[153],"test.":[156]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}