{"id":"https://openalex.org/W7165644676","doi":"https://doi.org/10.48550/arxiv.2606.21568","title":"A Smart Classroom Behavior Analysis Framework with a New Highly Congested Classroom Dataset","display_name":"A Smart Classroom Behavior Analysis Framework with a New Highly Congested Classroom Dataset","publication_year":2026,"publication_date":"2026-06-19","ids":{"openalex":"https://openalex.org/W7165644676","doi":"https://doi.org/10.48550/arxiv.2606.21568"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.21568","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21568","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.21568","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139209653","display_name":"Wei Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060082727","display_name":"\u50a8\u8302\u7965","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chu, Maoxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139195230","display_name":"Yuelong Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Yuelong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139214724","display_name":"Guanghao Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Guanghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055083769","display_name":"Yinxiang Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yinxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139139399","display_name":"Zhi Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139208155","display_name":"Haotian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haotian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5021436608","display_name":"Y C Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yutian","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":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":false,"primary_topic":{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.1014999970793724,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.1014999970793724,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.07909999787807465,"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/T10985","display_name":"Sleep and Wakefulness Research","score":0.061000000685453415,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5824000239372253},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.46230000257492065},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4207000136375427},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.40709999203681946},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.39910000562667847},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.36000001430511475},{"id":"https://openalex.org/keywords/behavioral-analysis","display_name":"Behavioral analysis","score":0.35260000824928284},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.33489999175071716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487999796867371},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5824000239372253},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45890000462532043},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4207000136375427},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3727000057697296},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.36000001430511475},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.353300005197525},{"id":"https://openalex.org/C2989277270","wikidata":"https://www.wikidata.org/wiki/Q168338","display_name":"Behavioral analysis","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.33489999175071716},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.334199994802475},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.21568","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21568","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.21568","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21568","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8803229331970215}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Student":[0],"behavior":[1,34,60,173,206],"detection":[2,103,174],"is":[3],"important":[4],"for":[5,202],"intelligent":[6],"classroom":[7,33,172,205],"analysis":[8],"but":[9],"remains":[10],"challenging":[11,80],"in":[12,29],"large-class":[13],"scenarios":[14],"due":[15],"to":[16,41,106],"dense":[17,84],"instance":[18],"co-occurrence,":[19],"asymmetric":[20],"occlusion,":[21,87],"depth-wise":[22],"scale":[23,88],"variation,":[24,89],"and":[25,36,43,74,90,144,148,159,166,188],"fine-grained":[26,91],"semantic":[27],"degradation":[28],"distant":[30],"targets.":[31],"Existing":[32],"datasets":[35,175],"general-purpose":[37],"detectors":[38],"are":[39],"insufficient":[40],"characterize":[42],"address":[44,95],"these":[45,96],"challenges.":[46],"This":[47],"paper":[48],"constructs":[49],"the":[50,118,130,149,196,199],"Highly":[51],"Congested":[52],"Classroom":[53],"Behavior":[54],"(HCCB)":[55],"dataset,":[56],"containing":[57],"50,229":[58],"student":[59],"instances":[61],"across":[62],"seven":[63],"categories:":[64],"reading,":[65],"writing,":[66],"heads":[67],"up,":[68],"sleeping,":[69],"looking":[70],"around,":[71],"bowing":[72],"head,":[73],"using":[75],"phone.":[76],"HCCB":[77,187],"provides":[78],"a":[79,101],"benchmark":[81],"that":[82,177],"integrates":[83,137],"distributions,":[85],"severe":[86],"behavioral":[92],"semantics.":[93],"To":[94],"issues,":[97],"we":[98],"propose":[99],"ODER-HSFNet,":[100],"YOLO-based":[102],"framework":[104],"tailored":[105],"highly":[107,203],"crowded":[108,204],"classrooms.":[109],"At":[110],"its":[111],"core,":[112],"ODER-HSFNet":[113,178],"introduces":[114],"three":[115],"task-specific":[116],"innovations:":[117],"Occlusion-aware":[119],"Deformable":[120],"Edge":[121],"Rectifier":[122],"(ODER),":[123],"which":[124,136,154],"strengthens":[125],"boundary":[126],"evidence":[127],"under":[128],"occlusion;":[129],"Hypergraph-State":[131],"Spatial":[132],"Fusion":[133],"(HSSF)":[134],"module,":[135],"local":[138],"structure":[139],"enhancement,":[140],"state-space":[141],"contextual":[142],"modeling,":[143],"high-order":[145],"relation":[146],"aggregation;":[147],"Occlusion-Calibrated":[150],"Detection":[151],"Head":[152],"(OCDetect),":[153],"suppresses":[155],"low-quality":[156],"Pre-NMS":[157],"candidates":[158],"reduces":[160],"false":[161],"positives":[162],"from":[163],"occlusion":[164],"boundaries":[165],"neighboring":[167],"instances.":[168],"Experiments":[169],"on":[170,186,190],"two":[171],"show":[176],"outperforms":[179],"mainstream":[180],"YOLO-series":[181],"methods,":[182],"achieving":[183],"60.60%/80.12%":[184],"mAP50:95/mAP50":[185],"57.36%/74.65%":[189],"SCB-D3-S.":[191],"Ablation":[192],"studies":[193],"further":[194],"verify":[195],"effectiveness":[197],"of":[198],"proposed":[200],"design":[201],"detection.":[207]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}
