{"id":"https://openalex.org/W3045455261","doi":"https://doi.org/10.1109/tip.2021.3055632","title":"A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds","display_name":"A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3045455261","doi":"https://doi.org/10.1109/tip.2021.3055632","mag":"3045455261","pmid":"https://pubmed.ncbi.nlm.nih.gov/33539297"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2021.3055632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3055632","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2007.12831","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yi Wang","orcid":"https://orcid.org/0000-0001-8659-4724"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yi Wang","raw_affiliation_strings":["School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-8659-4724","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Junhui Hou","orcid":"https://orcid.org/0000-0003-3431-2021"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Junhui Hou","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0003-3431-2021","affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinyu Hou","orcid":"https://orcid.org/0000-0001-8988-2422"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Xinyu Hou","raw_affiliation_strings":["School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-8988-2422","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lap-Pui Chau","orcid":"https://orcid.org/0000-0003-4932-0593"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Lap-Pui Chau","raw_affiliation_strings":["School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0003-4932-0593","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.7323,"has_fulltext":false,"cited_by_count":119,"citation_normalized_percentile":{"value":0.98450025,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"30","issue":null,"first_page":"2876","last_page":"2887"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6675000190734863,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.6675000190734863,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.09160000085830688,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.05950000137090683,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/object-detection","display_name":"Object detection","score":0.7196999788284302},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6890000104904175},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6194000244140625},{"id":"https://openalex.org/keywords/crowds","display_name":"Crowds","score":0.61080002784729},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5212000012397766},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5027999877929688},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.49639999866485596},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4880000054836273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7236999869346619},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7196999788284302},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6890000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6388999819755554},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6194000244140625},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.61080002784729},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.539900004863739},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5212000012397766},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5027999877929688},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.49639999866485596},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4880000054836273},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4869999885559082},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47870001196861267},{"id":"https://openalex.org/C38785706","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Interest point detection","level":5,"score":0.31779998540878296},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2526000142097473},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.25049999356269836}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tip.2021.3055632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3055632","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:33539297","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33539297","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":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:2007.12831","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.12831","pdf_url":"https://arxiv.org/pdf/2007.12831","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:dr.ntu.edu.sg:10356/160520","is_oa":false,"landing_page_url":"https://hdl.handle.net/10356/160520","pdf_url":null,"source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal Article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2007.12831","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.12831","pdf_url":"https://arxiv.org/pdf/2007.12831","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4563906475","display_name":null,"funder_award_id":"CityU 11219019","funder_id":"https://openalex.org/F4320321592","funder_display_name":"Research Grants Council, University Grants Committee"},{"id":"https://openalex.org/G5119697380","display_name":null,"funder_award_id":"CityU 11202320","funder_id":"https://openalex.org/F4320321592","funder_display_name":"Research Grants Council, University Grants Committee"}],"funders":[{"id":"https://openalex.org/F4320321592","display_name":"Research Grants Council, University Grants Committee","ror":"https://ror.org/00djwmt25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1910776219","https://openalex.org/W1982925187","https://openalex.org/W2079023123","https://openalex.org/W2098693229","https://openalex.org/W2123175289","https://openalex.org/W2147221461","https://openalex.org/W2161086211","https://openalex.org/W2194775991","https://openalex.org/W2209882149","https://openalex.org/W2225887246","https://openalex.org/W2463631526","https://openalex.org/W2541389513","https://openalex.org/W2579152745","https://openalex.org/W2729018917","https://openalex.org/W2883929025","https://openalex.org/W2884367402","https://openalex.org/W2896421256","https://openalex.org/W2915476573","https://openalex.org/W2938694836","https://openalex.org/W2948513880","https://openalex.org/W2962832028","https://openalex.org/W2962921175","https://openalex.org/W2963172723","https://openalex.org/W2963351448","https://openalex.org/W2963404857","https://openalex.org/W2963516811","https://openalex.org/W2963566548","https://openalex.org/W2963838390","https://openalex.org/W2964209782","https://openalex.org/W2964277612","https://openalex.org/W2971900262","https://openalex.org/W2987988567","https://openalex.org/W2989604896","https://openalex.org/W3007175960","https://openalex.org/W3027606690","https://openalex.org/W3035729912","https://openalex.org/W3106732900","https://openalex.org/W6620707391","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6681368121","https://openalex.org/W6684191040","https://openalex.org/W6754034799","https://openalex.org/W6754756387","https://openalex.org/W6760424586","https://openalex.org/W6770982990","https://openalex.org/W6776069494","https://openalex.org/W6779292872"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"article,":[2],"we":[3,42,65,91,131],"propose":[4,92,132],"a":[5,13,61,87,93],"novel":[6],"self-training":[7],"approach":[8,152],"named":[9],"Crowd-SDNet":[10],"that":[11,108,150],"enables":[12],"typical":[14],"object":[15,68,84,105],"detector":[16,113],"trained":[17],"only":[18],"with":[19,26,209],"point-level":[20,72],"annotations":[21,47],"(i.e.,":[22,196,204],"objects":[23,57,121],"are":[24,76],"labeled":[25],"points)":[27],"to":[28,48,79,99,117,126,137],"estimate":[29],"both":[30,159],"the":[31,44,50,53,71,81,102,109,112,139,146,168,177,186,190],"center":[32,54],"points":[33,55],"and":[34,95,119,161,175,193,198,200,206],"sizes":[35,69,85,106],"of":[36,52,56,83,111],"crowded":[37,129],"objects.":[38],"Specifically,":[39],"during":[40],"training,":[41],"utilize":[43],"available":[45,218],"point":[46],"supervise":[49],"estimation":[51],"directly.":[58],"Based":[59],"on":[60,145,189],"locally-uniform":[62],"distribution":[63],"assumption,":[64],"initialize":[66],"pseudo":[67,104],"from":[70],"supervisory":[73],"information,":[74],"which":[75],"then":[77],"leveraged":[78],"guide":[80],"regression":[82],"via":[86],"crowdedness-aware":[88],"loss.":[89],"Meanwhile,":[90],"confidence":[94],"order-aware":[96],"refinement":[97],"scheme":[98],"continuously":[100],"refine":[101],"initial":[103],"such":[107],"ability":[110],"is":[114],"increasingly":[115],"boosted":[116],"detect":[118],"count":[120],"in":[122],"crowds":[123],"simultaneously.":[124],"Moreover,":[125],"address":[127],"extremely":[128],"scenes,":[130],"an":[133],"effective":[134],"decoding":[135],"method":[136,166,184],"improve":[138],"detector's":[140],"representation":[141],"ability.":[142],"Experimental":[143],"results":[144,188],"WiderFace":[147],"benchmark":[148],"show":[149],"our":[151,165,183],"significantly":[153],"outperforms":[154],"state-of-the-art":[155,210],"point-supervised":[156],"methods":[157],"under":[158],"detection":[160],"counting":[162,178,192,202],"tasks,":[163],"i.e.,":[164],"improves":[167],"average":[169],"precision":[170],"by":[171,180],"more":[172],"than":[173],"10%":[174],"reduces":[176],"error":[179],"31.2%.":[181],"Besides,":[182],"obtains":[185],"best":[187],"crowd":[191],"localization":[194],"datasets":[195,203],"ShanghaiTech":[197],"NWPU-Crowd)":[199],"vehicle":[201],"CARPK":[205],"PUCPR+)":[207],"compared":[208],"counting-by-detection":[211],"methods.":[212],"The":[213],"code":[214],"will":[215],"be":[216],"publicly":[217],"at":[219],"https://github.com/WangyiNTU/Point-supervised-crowd-detection.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":25},{"year":2024,"cited_by_count":35},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":26},{"year":2021,"cited_by_count":7}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2020-07-29T00:00:00"}
