{"id":"https://openalex.org/W7118813651","doi":"https://doi.org/10.48550/arxiv.2601.00391","title":"Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models","display_name":"Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7118813651","doi":"https://doi.org/10.48550/arxiv.2601.00391"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.00391","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00391","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.00391","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"AlDahoul, Nouar","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"AlDahoul, Nouar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Sabri, Aznul Qalid Md","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sabri, Aznul Qalid Md","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Mansoor, Ali Mohammed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mansoor, Ali Mohammed","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":true,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.4925000071525574,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.4925000071525574,"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":0.2190999984741211,"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":0.11840000003576279,"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/discriminative-model","display_name":"Discriminative model","score":0.734000027179718},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.701200008392334},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6420000195503235},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6306999921798706},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6080999970436096},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.54830002784729},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5073999762535095},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.4036000072956085},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.39250001311302185}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8219000101089478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7904999852180481},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.734000027179718},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.701200008392334},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6420000195503235},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6306999921798706},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6080999970436096},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.54830002784729},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4860999882221222},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.4036000072956085},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33820000290870667},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3041999936103821},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C2779769447","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Foreground detection","level":4,"score":0.2824999988079071},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.275299996137619},{"id":"https://openalex.org/C202227193","wikidata":"https://www.wikidata.org/wiki/Q6345568","display_name":"Kadir\u2013Brady saliency detector","level":4,"score":0.2741999924182892},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2603999972343445},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.2554999887943268},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.00391","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00391","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.00391","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00391","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.6772481799125671,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Human":[0],"detection":[1,111,183],"in":[2,8,44,112,147,231],"videos":[3,113],"plays":[4],"an":[5,120,189,196,212],"important":[6],"role":[7],"various":[9],"real-life":[10],"applications.":[11],"Most":[12],"traditional":[13],"approaches":[14,55],"depend":[15],"on":[16,119,132],"utilizing":[17],"handcrafted":[18],"features,":[19],"which":[20,85],"are":[21,30,56,128,178],"problem-dependent":[22],"and":[23,42,58,63,89,104,130,136,153,169,203],"optimal":[24],"for":[25,109,180],"specific":[26],"tasks.":[27],"Moreover,":[28],"they":[29],"highly":[31,61,137],"susceptible":[32],"to":[33],"dynamical":[34],"events":[35],"such":[36],"as":[37],"illumination":[38],"changes,":[39],"camera":[40,118],"jitter,":[41],"variations":[43],"object":[45],"sizes.":[46],"On":[47],"the":[48,51,71,133,175,181],"other":[49],"hand,":[50],"proposed":[52,176],"feature":[53,82,102],"learning":[54,83,107,154],"cheaper":[57],"easier":[59],"because":[60],"abstract":[62],"discriminative":[64],"features":[65],"can":[66],"be":[67],"produced":[68],"automatically":[69],"without":[70],"need":[72],"of":[73,149,192,199,215],"expert":[74],"knowledge.":[75],"In":[76],"this":[77],"paper,":[78],"we":[79],"utilize":[80],"automatic":[81],"methods,":[84],"combine":[86],"optical":[87],"flow":[88],"three":[90],"different":[91],"deep":[92],"models":[93,127,146],"(i.e.,":[94],"supervised":[95],"convolutional":[96],"neural":[97],"network":[98],"(S-CNN),":[99],"pretrained":[100,186],"CNN":[101,187],"extractor,":[103],"hierarchical":[105],"extreme":[106],"machine)":[108],"human":[110,163,182],"captured":[114],"using":[115],"a":[116,218,237],"nonstatic":[117],"aerial":[121,140],"platform":[122],"with":[123,201,205,236],"varying":[124],"altitudes.":[125],"The":[126,142,158,185],"trained":[129],"tested":[131],"publicly":[134],"available":[135],"challenging":[138],"UCF-ARG":[139],"dataset.":[141],"comparison":[143],"between":[144],"these":[145],"terms":[148],"training,":[150],"testing":[151],"accuracy,":[152],"speed":[155],"is":[156],"analyzed.":[157],"performance":[159],"evaluation":[160],"considers":[161],"five":[162],"actions":[164],"(digging,":[165],"waving,":[166],"throwing,":[167],"walking,":[168],"running).":[170],"Experimental":[171],"results":[172],"demonstrated":[173],"that":[174],"methods":[177],"successful":[179],"task.":[184],"produces":[188,195],"average":[190,197,213],"accuracy":[191,198,214],"98.09%.":[193],"S-CNN":[194,232],"95.6%":[200],"softmax":[202],"91.7%":[204],"Support":[206],"Vector":[207],"Machines":[208],"(SVM).":[209],"H-ELM":[210],"has":[211],"95.9%.":[216],"Using":[217],"normal":[219],"Central":[220],"Processing":[221,240],"Unit":[222,241],"(CPU),":[223],"H-ELM's":[224],"training":[225],"time":[226],"takes":[227,233],"445":[228],"seconds.":[229],"Learning":[230],"770":[234],"seconds":[235],"high-performance":[238],"Graphical":[239],"(GPU).":[242]},"counts_by_year":[],"updated_date":"2025-11-06T04:12:42.849631","created_date":"2025-10-10T00:00:00"}
