{"id":"https://openalex.org/W7161679009","doi":"https://doi.org/10.48550/arxiv.2605.16397","title":"Trajectory-Aware Adaptive Inference in Object Detection Models","display_name":"Trajectory-Aware Adaptive Inference in Object Detection Models","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161679009","doi":"https://doi.org/10.48550/arxiv.2605.16397"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16397","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.16397","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134865933","display_name":"Grigorios Papanikolaou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Papanikolaou, Grigorios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060088887","display_name":"Ioannis Kontopoulos","orcid":"https://orcid.org/0000-0001-9862-8944"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kontopoulos, Ioannis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054072214","display_name":"Giannis Spiliopoulos","orcid":"https://orcid.org/0000-0002-5003-7923"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Spiliopoulos, Giannis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136462918","display_name":"Dimitris Zissis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zissis, Dimitris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136491328","display_name":"Konstantinos Tserpes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tserpes, Konstantinos","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/T11622","display_name":"Maritime Navigation and Safety","score":0.5827000141143799,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T11622","display_name":"Maritime Navigation and Safety","score":0.5827000141143799,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.265500009059906,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.02329999953508377,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7276999950408936},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7081999778747559},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6248000264167786},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5792999863624573},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5285000205039978},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5120000243186951},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47600001096725464},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.41200000047683716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7495999932289124},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7276999950408936},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7081999778747559},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6704000234603882},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6585999727249146},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6248000264167786},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5792999863624573},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5285000205039978},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5120000243186951},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C2780624872","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion detection","level":3,"score":0.31949999928474426},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.3077999949455261},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.28870001435279846},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.25369998812675476},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2522999942302704},{"id":"https://openalex.org/C172849965","wikidata":"https://www.wikidata.org/wiki/Q3148875","display_name":"Reference frame","level":3,"score":0.25099998712539673},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16397","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.16397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16397","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,130],"increasing":[1],"integration":[2],"of":[3,30,47,101,123,136,141],"sensors":[4],"in":[5,17,37,87],"autonomous":[6],"maritime":[7,42],"navigation":[8],"has":[9],"led":[10],"to":[11,77,189],"large-scale":[12],"multimodal":[13],"datasets,":[14],"raising":[15],"challenges":[16],"achieving":[18],"efficient":[19],"real-time":[20],"perception.":[21],"In":[22],"such":[23,40,96],"systems,":[24],"object":[25,48,65],"detection":[26,49,66,169],"and":[27,151,176,186],"trajectory":[28,71],"perception":[29],"nearby":[31],"vessels":[32,102],"are":[33,104,113],"tightly":[34],"coupled,":[35],"particularly":[36],"dynamic":[38],"environments":[39],"as":[41,97],"navigation.":[43],"However,":[44],"the":[45,74,116,124,152,156],"efficiency":[46,187],"models":[50],"during":[51],"inference":[52,75,174],"remains":[53],"an":[54,63,84],"often-overlooked":[55],"aspect.":[56],"To":[57],"this":[58,165],"end,":[59],"we":[60,82],"build":[61],"upon":[62],"existing":[64],"framework":[67],"by":[68,106,147],"incorporating":[69],"GPS":[70],"data":[72],"into":[73],"process":[76],"enable":[78],"input-adaptive":[79],"computation.":[80],"Specifically,":[81],"introduce":[83],"early-exit":[85],"mechanism":[86],"a":[88,121,137,181],"YOLOv8-based":[89],"detector":[90],"that":[91,103,164],"incorporates":[92],"motion":[93],"cues":[94],"-":[95],"inter-vessel":[98],"distances.":[99],"Frames":[100],"separated":[105],"short":[107],"distances,":[108],"converging":[109],"with":[110],"high":[111],"speed,":[112],"processed":[114],"using":[115],"full":[117],"model,":[118],"while":[119,171],"only":[120],"subset":[122],"network's":[125],"architecture":[126],"is":[127,145],"activated":[128],"otherwise.":[129],"difficulty":[131],"degree":[132],"(or":[133],"scene":[134],"complexity)":[135],"frame":[138],"or":[139],"set":[140],"frames":[142],"per":[143],"second":[144],"evaluated":[146],"leveraging":[148],"inter-object":[149],"distance":[150,157],"rate":[153],"at":[154],"which":[155],"between":[158,184],"them":[159],"decreases.":[160],"Experimental":[161],"results":[162],"demonstrate":[163],"strategy":[166],"maintains":[167],"satisfactory":[168],"performance":[170],"significantly":[172],"reducing":[173],"time":[175],"computational":[177],"cost,":[178],"thus":[179],"enabling":[180],"flexible":[182],"trade-off":[183],"accuracy":[185],"compared":[188],"full-model":[190],"inference.":[191]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
