{"id":"https://openalex.org/W4313124705","doi":"https://doi.org/10.1016/j.procs.2022.09.053","title":"Monocular depth-based visual tracker for gait recognition","display_name":"Monocular depth-based visual tracker for gait recognition","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4313124705","doi":"https://doi.org/10.1016/j.procs.2022.09.053"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2022.09.053","doi":"https://doi.org/10.1016/j.procs.2022.09.053","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2022.09.053","pdf_url":"https://www.sciencedirect.com/science/article/pii/S1877050922009267/pdf","source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.sciencedirect.com/science/article/pii/S1877050922009267/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053328508","display_name":"Margarita N. Favorskaya","orcid":"https://orcid.org/0000-0002-2181-0454","observed_orcids":["https://orcid.org/0000-0002-2181-0454"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Margarita N. Favorskaya","raw_affiliation_strings":["Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy ave., Krasnoyarsk, 660037 Russian Federation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy ave., Krasnoyarsk, 660037 Russian Federation","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038202974","display_name":"\u0411\u0443\u0440\u044f\u0447\u0435\u043d\u043a\u043e \u0412\u043b\u0430\u0434\u0438\u043c\u0438\u0440 \u0412\u0438\u043a\u0442\u043e\u0440\u043e\u0432\u0438\u0447","orcid":"https://orcid.org/0000-0003-1151-1159","observed_orcids":["https://orcid.org/0000-0003-1151-1159"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vladimir V. Buryachenko","raw_affiliation_strings":["Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy ave., Krasnoyarsk, 660037 Russian Federation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy ave., Krasnoyarsk, 660037 Russian Federation","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5053328508"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4255,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.47401646,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":"207","issue":null,"first_page":"205","last_page":"214"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9160000085830688,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9160000085830688,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.03290000185370445,"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":0.01489999983459711,"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/gait-recognition","display_name":"gait recognition","score":0.9490000009536743},{"id":"https://openalex.org/keywords/monocular-depth-estimation","display_name":"monocular depth estimation","score":0.9789999723434448},{"id":"https://openalex.org/keywords/visual-tracking","display_name":"visual object tracking","score":0.6029999852180481},{"id":"https://openalex.org/keywords/ordinal-regression","display_name":"ordinal regression","score":0.8349999785423279},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.753000020980835},{"id":"https://openalex.org/keywords/distance-estimation","display_name":"distance estimation","score":0.6240000128746033},{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.3199999928474426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8443325757980347},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8086910247802734},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7256081700325012},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5927436947822571},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5862839221954346},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.5190539956092834},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47277429699897766},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3936012387275696},{"id":"https://openalex.org/C42407357","wikidata":"https://www.wikidata.org/wiki/Q521","display_name":"Physiology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2022.09.053","doi":"https://doi.org/10.1016/j.procs.2022.09.053","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2022.09.053","pdf_url":"https://www.sciencedirect.com/science/article/pii/S1877050922009267/pdf","source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2022.09.053","doi":"https://doi.org/10.1016/j.procs.2022.09.053","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2022.09.053","pdf_url":"https://www.sciencedirect.com/science/article/pii/S1877050922009267/pdf","source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4313124705.pdf","grobid_xml":"https://content.openalex.org/works/W4313124705.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1905829557","https://openalex.org/W2133665775","https://openalex.org/W2150066425","https://openalex.org/W2427448504","https://openalex.org/W2520707372","https://openalex.org/W2534225927","https://openalex.org/W2608018946","https://openalex.org/W2609883120","https://openalex.org/W2731821979","https://openalex.org/W2806446538","https://openalex.org/W2884367402","https://openalex.org/W2894809939","https://openalex.org/W2934279571","https://openalex.org/W2963412495","https://openalex.org/W2963583471","https://openalex.org/W2963654727","https://openalex.org/W2963781481","https://openalex.org/W2964020152","https://openalex.org/W2969556351","https://openalex.org/W2970369664","https://openalex.org/W3018757597","https://openalex.org/W3036108163","https://openalex.org/W3037743604","https://openalex.org/W3118635606","https://openalex.org/W3185748252","https://openalex.org/W4244531973","https://openalex.org/W4245551996","https://openalex.org/W4247924304","https://openalex.org/W4249142012","https://openalex.org/W4255556797","https://openalex.org/W4286696412","https://openalex.org/W4287964610","https://openalex.org/W4297683907","https://openalex.org/W4301045096"],"related_works":["https://openalex.org/W1970891762","https://openalex.org/W3033060862","https://openalex.org/W4415538800","https://openalex.org/W623540751","https://openalex.org/W2888795925","https://openalex.org/W2995927862","https://openalex.org/W75119315","https://openalex.org/W2888043061","https://openalex.org/W4246474087","https://openalex.org/W6922601166"],"abstract_inverted_index":{"Recognition":[0],"of":[1,17,28,74,90,113,131,138,184],"human":[2],"gait":[3,92,154,171],"in":[4,31,36,116],"the":[5,24,34,40,91,97,111,117,136,158,182,190],"wild":[6],"remains":[7],"a":[8,29,125,139,160,196],"weakly":[9],"studied":[10],"problem":[11,94],"due":[12],"to":[13,22,33,38,101,110],"many":[14],"factors.":[15],"One":[16],"these":[18],"factors":[19],"is":[20,77,95,133,162],"how":[21],"estimate":[23],"distance":[25,63,72,159,198],"and":[26,82,174],"location":[27],"person":[30,161],"relation":[32],"camera":[35],"order":[37],"find":[39],"best":[41],"position":[42],"for":[43,79,105,119],"visual":[44,140],"capturing.":[45],"Urban":[46],"video":[47],"surveillance":[48],"usually":[49],"does":[50],"not":[51],"use":[52,112,183],"additional":[53],"sensors,":[54],"such":[55,185],"as":[56,145],"RGB-D":[57],"cameras,":[58],"LiDAR":[59],"sensors":[60],"or":[61,153],"other":[62],"detection":[64],"devices.":[65],"Therefore,":[66],"scene":[67],"depth":[68,98,121,126],"estimation":[69,73],"followed":[70],"by":[71],"moving":[75,193],"objects":[76,194],"useful":[78],"their":[80],"capturing":[81],"tracking":[83],"before":[84,149],"being":[85],"recognized.":[86],"A":[87],"special":[88],"feature":[89],"recognition":[93,152,172],"that":[96,181],"map":[99],"needs":[100],"be":[102],"adjusted":[103],"only":[104],"close":[106],"distance.":[107],"This":[108,143],"leads":[109],"ordinal":[114,127],"regression":[115,128],"model":[118],"monocular":[120],"estimation.":[122],"We":[123],"offer":[124],"CNN,":[129],"output":[130,137],"which":[132],"concatenated":[134],"with":[135],"deep-based":[141],"detector.":[142],"works":[144],"an":[146,186],"initial":[147,187],"stage":[148],"walking":[150],"style":[151],"recognition,":[155],"depending":[156],"on":[157],"moving.":[163],"The":[164],"experiments":[165],"were":[166],"conducted":[167],"using":[168],"publicity":[169],"available":[170],"datasets":[173],"own":[175],"dataset.":[176],"It":[177],"has":[178],"been":[179],"shown":[180],"procedure":[188],"increases":[189],"accuracy,":[191],"since":[192],"from":[195],"large":[197],"are":[199],"preventively":[200],"rejected.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2023-01-06T00:00:00"}
