{"id":"https://openalex.org/W4315471928","doi":"https://doi.org/10.1109/icarcv57592.2022.10004223","title":"Center Keypoint for Parking Slot Detection with Self-Calibrated Convolutions Network","display_name":"Center Keypoint for Parking Slot Detection with Self-Calibrated Convolutions Network","publication_year":2022,"publication_date":"2022-12-11","ids":{"openalex":"https://openalex.org/W4315471928","doi":"https://doi.org/10.1109/icarcv57592.2022.10004223"},"language":"en","primary_location":{"id":"doi:10.1109/icarcv57592.2022.10004223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv57592.2022.10004223","pdf_url":null,"source":{"id":"https://openalex.org/S4363608251","display_name":"2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5109998663","display_name":"Ruitao Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruitao Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055131312","display_name":"Shikang Lian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shikang Lian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103105049","display_name":"Weihao Liang","orcid":"https://orcid.org/0000-0002-1088-583X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weihao Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044292017","display_name":"Yaze Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yaze Tang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088723765","display_name":"Wei Meng","orcid":"https://orcid.org/0000-0003-0209-8753"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Meng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0816,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.87955705,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"23 5","issue":null,"first_page":"305","last_page":"310"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12546","display_name":"Smart Parking Systems Research","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12546","display_name":"Smart Parking Systems Research","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.9915000200271606,"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":0.7270715832710266},{"id":"https://openalex.org/keywords/parking-lot","display_name":"Parking lot","score":0.6582843065261841},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.6167178750038147},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.6130356788635254},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.612524688243866},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5878414511680603},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.577022910118103},{"id":"https://openalex.org/keywords/recall-rate","display_name":"Recall rate","score":0.5689360499382019},{"id":"https://openalex.org/keywords/center","display_name":"Center (category theory)","score":0.560509204864502},{"id":"https://openalex.org/keywords/occupancy-grid-mapping","display_name":"Occupancy grid mapping","score":0.5404603481292725},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5091745257377625},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47573065757751465},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45187705755233765},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.417605459690094},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3245154619216919},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10080081224441528},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07450434565544128}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7270715832710266},{"id":"https://openalex.org/C2777427512","wikidata":"https://www.wikidata.org/wiki/Q6501349","display_name":"Parking lot","level":2,"score":0.6582843065261841},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.6167178750038147},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.6130356788635254},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.612524688243866},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5878414511680603},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.577022910118103},{"id":"https://openalex.org/C2987098735","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"Recall rate","level":2,"score":0.5689360499382019},{"id":"https://openalex.org/C2779463800","wikidata":"https://www.wikidata.org/wiki/Q5062222","display_name":"Center (category theory)","level":2,"score":0.560509204864502},{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.5404603481292725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5091745257377625},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47573065757751465},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45187705755233765},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.417605459690094},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3245154619216919},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10080081224441528},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07450434565544128},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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},{"id":"https://openalex.org/C8010536","wikidata":"https://www.wikidata.org/wiki/Q160398","display_name":"Crystallography","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.0},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icarcv57592.2022.10004223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv57592.2022.10004223","pdf_url":null,"source":{"id":"https://openalex.org/S4363608251","display_name":"2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2182666667","https://openalex.org/W2560544142","https://openalex.org/W2802492649","https://openalex.org/W2887902357","https://openalex.org/W2965287913","https://openalex.org/W2989604896","https://openalex.org/W3012573144","https://openalex.org/W3016100773","https://openalex.org/W3034752215","https://openalex.org/W3044417735","https://openalex.org/W3118576105","https://openalex.org/W3119069671","https://openalex.org/W4293584584","https://openalex.org/W4379988230","https://openalex.org/W6750227808","https://openalex.org/W6760424586","https://openalex.org/W6774855280","https://openalex.org/W6800178323"],"related_works":["https://openalex.org/W2162255319","https://openalex.org/W3033776171","https://openalex.org/W1999050017","https://openalex.org/W5037887","https://openalex.org/W4229444815","https://openalex.org/W4293877624","https://openalex.org/W2601891598","https://openalex.org/W2121569490","https://openalex.org/W2352065863","https://openalex.org/W4324088501"],"abstract_inverted_index":{"Available":[0],"parking":[1,10,19,35,57,92,165],"slot":[2,20,58,93],"detection":[3,21,103],"is":[4],"the":[5,25,30,34,46,56,62,75,90,107,114,125,128,132,140,168],"first":[6],"step":[7],"for":[8,86],"autonomous":[9],"systems.":[11],"In":[12],"this":[13],"paper,":[14],"we":[15,122],"propose":[16],"a":[17,39,83,143,148],"novel":[18],"method":[22,66,130],"that":[23],"uses":[24],"center":[26,115],"information":[27,101],"regression":[28],"and":[29,50,81,102,110,118,159],"occupancy":[31,49,154],"classification":[32,155],"of":[33,127,146,151,157,164],"slot.":[36],"We":[37,105],"design":[38],"self-calibrated":[40],"convolutions":[41],"network":[42],"(SCCN)":[43],"to":[44,61,112],"obtain":[45],"position,":[47],"length,":[48],"direction,":[51],"which":[52],"can":[53,94],"also":[54],"infer":[55],"type":[59],"according":[60],"prediction":[63],"results.":[64,104],"The":[65,136],"divides":[67],"an":[68,153],"around":[69],"view":[70],"monitor":[71],"(AVM)":[72],"image":[73],"into":[74],"16":[76,78],"\u00d7":[77],"grid":[79],"cells":[80],"performs":[82],"SCCN":[84],"detector":[85],"feature":[87],"extraction.":[88],"Subsequently,":[89],"whole":[91],"be":[95],"easily":[96],"inferred":[97,162],"via":[98],"prior":[99],"geometric":[100],"adopt":[106],"heatmap,":[108],"MultiBins,":[109],"midline":[111],"detect":[113],"keypoint,":[116],"direction":[117],"occupancy,":[119],"respectively.":[120],"And":[121],"quantitatively":[123],"evaluate":[124],"performance":[126],"proposed":[129],"on":[131,167],"public":[133],"PS2.0":[134],"datasets.":[135,169],"experimental":[137],"results":[138],"show":[139],"outperformance":[141],"by":[142],"precision":[144],"rate":[145,150],"99.35%,":[147],"recall":[149],"99.17%,":[152],"accuracy":[156],"99.12%":[158],"all":[160],"correctly":[161],"types":[163],"slots":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}