{"id":"https://openalex.org/W4386838005","doi":"https://doi.org/10.3390/s23187937","title":"An H-GrabCut Image Segmentation Algorithm for Indoor Pedestrian Background Removal","display_name":"An H-GrabCut Image Segmentation Algorithm for Indoor Pedestrian Background Removal","publication_year":2023,"publication_date":"2023-09-16","ids":{"openalex":"https://openalex.org/W4386838005","doi":"https://doi.org/10.3390/s23187937","pmid":"https://pubmed.ncbi.nlm.nih.gov/37765994"},"language":"en","primary_location":{"id":"doi:10.3390/s23187937","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23187937","pdf_url":"https://www.mdpi.com/1424-8220/23/18/7937/pdf?version=1694858358","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/23/18/7937/pdf?version=1694858358","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010234490","display_name":"Xuchao Huang","orcid":"https://orcid.org/0009-0009-1316-717X"},"institutions":[{"id":"https://openalex.org/I18570673","display_name":"Guangxi University of Science and Technology","ror":"https://ror.org/02fj6b627","country_code":"CN","type":"education","lineage":["https://openalex.org/I18570673"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuchao Huang","raw_affiliation_strings":["Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","institution_ids":[]},{"raw_affiliation_string":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China","institution_ids":["https://openalex.org/I18570673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033598490","display_name":"Shigang Wang","orcid":"https://orcid.org/0000-0002-6868-6542"},"institutions":[{"id":"https://openalex.org/I18570673","display_name":"Guangxi University of Science and Technology","ror":"https://ror.org/02fj6b627","country_code":"CN","type":"education","lineage":["https://openalex.org/I18570673"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shigang Wang","raw_affiliation_strings":["Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","institution_ids":[]},{"raw_affiliation_string":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China","institution_ids":["https://openalex.org/I18570673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100620849","display_name":"Xueshan Gao","orcid":"https://orcid.org/0000-0003-0656-9460"},"institutions":[{"id":"https://openalex.org/I18570673","display_name":"Guangxi University of Science and Technology","ror":"https://ror.org/02fj6b627","country_code":"CN","type":"education","lineage":["https://openalex.org/I18570673"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xueshan Gao","raw_affiliation_strings":["Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","institution_ids":[]},{"raw_affiliation_string":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China","institution_ids":["https://openalex.org/I18570673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076781173","display_name":"Dingji Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dingji Luo","raw_affiliation_strings":["Mechanical and Electrical College, Beijing Institute of Technology, Beijing 100190, China"],"affiliations":[{"raw_affiliation_string":"Mechanical and Electrical College, Beijing Institute of Technology, Beijing 100190, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101992607","display_name":"Weiye Xu","orcid":"https://orcid.org/0000-0002-0559-339X"},"institutions":[{"id":"https://openalex.org/I18570673","display_name":"Guangxi University of Science and Technology","ror":"https://ror.org/02fj6b627","country_code":"CN","type":"education","lineage":["https://openalex.org/I18570673"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiye Xu","raw_affiliation_strings":["Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","institution_ids":[]},{"raw_affiliation_string":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China","institution_ids":["https://openalex.org/I18570673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112125888","display_name":"Huiqing Pang","orcid":null},"institutions":[{"id":"https://openalex.org/I18570673","display_name":"Guangxi University of Science and Technology","ror":"https://ror.org/02fj6b627","country_code":"CN","type":"education","lineage":["https://openalex.org/I18570673"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiqing Pang","raw_affiliation_strings":["Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China","institution_ids":[]},{"raw_affiliation_string":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China","institution_ids":["https://openalex.org/I18570673"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084599546","display_name":"Ming Zhou","orcid":"https://orcid.org/0000-0001-7863-6437"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Zhou","raw_affiliation_strings":["Hangke Jinggong Co., Ltd., Beijing 102400, China"],"affiliations":[{"raw_affiliation_string":"Hangke Jinggong Co., Ltd., Beijing 102400, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5033598490","https://openalex.org/A5100620849"],"corresponding_institution_ids":["https://openalex.org/I18570673"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":0.3611,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60083429,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":"23","issue":"18","first_page":"7937","last_page":"7937"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9994999766349792,"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/T11019","display_name":"Image Enhancement Techniques","score":0.996999979019165,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7014089822769165},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6930044293403625},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6285461187362671},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6166175603866577},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.5612462759017944},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5366112589836121},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4804248809814453},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.47499266266822815},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.4561421871185303},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.43974992632865906},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3714601397514343},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12632063031196594}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7014089822769165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6930044293403625},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6285461187362671},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6166175603866577},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.5612462759017944},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5366112589836121},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4804248809814453},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.47499266266822815},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.4561421871185303},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.43974992632865906},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3714601397514343},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12632063031196594},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/s23187937","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23187937","pdf_url":"https://www.mdpi.com/1424-8220/23/18/7937/pdf?version=1694858358","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:37765994","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37765994","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10536006","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10536006","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10536006/pdf/sensors-23-07937.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:1307d29310c948e1827fcd848ec2e0d8","is_oa":true,"landing_page_url":"https://doaj.org/article/1307d29310c948e1827fcd848ec2e0d8","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 23, Iss 18, p 7937 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/s23187937","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23187937","pdf_url":"https://www.mdpi.com/1424-8220/23/18/7937/pdf?version=1694858358","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7099999785423279}],"awards":[],"funders":[{"id":"https://openalex.org/F4320313934","display_name":"Institut national de recherche en informatique et en automatique (INRIA)","ror":"https://ror.org/02kvxyf05"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386838005.pdf","grobid_xml":"https://content.openalex.org/works/W4386838005.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W2124351162","https://openalex.org/W2150721269","https://openalex.org/W2566376500","https://openalex.org/W2791710889","https://openalex.org/W2912076993","https://openalex.org/W2951051324","https://openalex.org/W2960571991","https://openalex.org/W2995081268","https://openalex.org/W3008354288","https://openalex.org/W3017171761","https://openalex.org/W3154792692","https://openalex.org/W3174428028","https://openalex.org/W3208409956","https://openalex.org/W4225124443","https://openalex.org/W4229440871","https://openalex.org/W4313472245","https://openalex.org/W4313892626","https://openalex.org/W4317738539","https://openalex.org/W4361007673","https://openalex.org/W4367016351"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W3122828758","https://openalex.org/W2101960027","https://openalex.org/W4205958986","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W2170799233","https://openalex.org/W1522196789"],"abstract_inverted_index":{"In":[0],"the":[1,17,62,78,96,104,108,138,159,173,189],"context":[2],"of":[3,36,61,99,107,134,149,163,176],"predicting":[4],"pedestrian":[5,180],"trajectories":[6],"for":[7,71],"indoor":[8,20,73,177],"mobile":[9,178],"robots,":[10],"it":[11],"is":[12],"crucial":[13],"to":[14,27,42,83,94],"accurately":[15],"measure":[16],"distance":[18,46],"between":[19],"pedestrians":[21,33],"and":[22,38,48,118,141,153,161,183],"robots.":[23],"This":[24],"study":[25],"aims":[26],"address":[28],"this":[29],"requirement":[30],"by":[31,111],"extracting":[32],"as":[34],"regions":[35],"interest":[37],"mitigating":[39],"issues":[40],"related":[41],"inaccurate":[43],"depth":[44],"camera":[45],"measurements":[47],"illumination":[49],"conditions.":[50],"To":[51],"tackle":[52],"these":[53],"challenges,":[54],"we":[55,76,88,102],"focus":[56],"on":[57,188],"an":[58,90],"improved":[59],"version":[60],"H-GrabCut":[63],"image":[64],"segmentation":[65,132],"algorithm,":[66],"which":[67],"involves":[68],"four":[69],"steps":[70],"segmenting":[72],"pedestrians.":[74,100],"Firstly,":[75],"leverage":[77],"YOLO-V5":[79],"object":[80],"recognition":[81],"algorithm":[82,93,110,129],"construct":[84],"detection":[85],"nodes.":[86],"Next,":[87],"propose":[89],"enhanced":[91],"BIL-MSRCR":[92],"enhance":[95],"edge":[97],"details":[98],"Finally,":[101],"optimize":[103],"clustering":[105],"features":[106],"GrabCut":[109],"incorporating":[112],"two-dimensional":[113],"entropy,":[114],"UV":[115],"component":[116],"distance,":[117],"LBP":[119],"texture":[120],"feature":[121],"values.":[122],"The":[123,166],"experimental":[124],"results":[125],"demonstrate":[126],"that":[127],"our":[128,164],"achieves":[130],"a":[131],"accuracy":[133],"97.13%":[135],"in":[136,147,172],"both":[137],"INRIA":[139],"dataset":[140],"real-world":[142],"tests,":[143],"outperforming":[144],"alternative":[145],"methods":[146],"terms":[148],"sensitivity,":[150],"missegmentation":[151],"rate,":[152],"intersection-over-union":[154],"metrics.":[155],"These":[156],"experiments":[157],"confirm":[158],"feasibility":[160],"practicality":[162],"approach.":[165],"aforementioned":[167],"findings":[168],"will":[169],"be":[170],"utilized":[171],"preliminary":[174],"processing":[175],"robot":[179],"trajectory":[181],"prediction":[182],"enable":[184],"path":[185],"planning":[186],"based":[187],"predicted":[190],"results.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
