{"id":"https://openalex.org/W7225834139","doi":"https://doi.org/10.48550/arxiv.2611.13488","title":"When Reconstruction Helps Event-Camera Person Triggers","display_name":"When Reconstruction Helps Event-Camera Person Triggers","publication_year":2026,"publication_date":"2026-11-10","ids":{"openalex":"https://openalex.org/W7225834139","doi":"https://doi.org/10.48550/arxiv.2611.13488"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2611.13488","doi":"https://doi.org/10.48550/arxiv.2611.13488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.13488","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2611.13488","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052729137","display_name":"Yifan Shuai","orcid":"https://orcid.org/0009-0006-1052-984X","observed_orcids":["https://orcid.org/0009-0006-1052-984X"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shuai, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5052729137"],"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.6913999915122986,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.6913999915122986,"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":0.13259999454021454,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.12080000340938568,"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":[],"concepts":[{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.6884999871253967},{"id":"https://openalex.org/C2778217198","wikidata":"https://www.wikidata.org/wiki/Q556977","display_name":"Cutoff","level":2,"score":0.6514000296592712},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5819000005722046},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5785999894142151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.574999988079071},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5174999833106995},{"id":"https://openalex.org/C2989486834","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"True positive rate","level":2,"score":0.5109000205993652},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.48919999599456787},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.4117000102996826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C112789634","wikidata":"https://www.wikidata.org/wiki/Q18207010","display_name":"False positives and false negatives","level":3,"score":0.2939999997615814},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C3019635856","wikidata":"https://www.wikidata.org/wiki/Q1619726","display_name":"Background image","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26429998874664307},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.2624000012874603},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25540000200271606},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2611.13488","doi":"https://doi.org/10.48550/arxiv.2611.13488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.13488","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2611.13488","doi":"https://doi.org/10.48550/arxiv.2611.13488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.13488","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Event":[0],"cameras":[1],"record":[2],"asynchronous":[3],"changes":[4],"in":[5,23],"brightness.":[6],"To":[7],"use":[8],"a":[9,14,17,24,96,177],"general":[10],"visual":[11],"model":[12,27],"as":[13],"recording":[15,169],"trigger,":[16],"system":[18],"must":[19],"represent":[20],"these":[21],"measurements":[22],"form":[25],"the":[26,133,147,151,163,190],"can":[28],"interpret.":[29],"We":[30],"compare":[31],"direct":[32,77,112,149],"grayscale":[33],"accumulation,":[34],"two":[35,39],"temporal":[36,78],"encodings,":[37],"and":[38,67,88,106,119,143,168,186,192],"intensity":[40],"reconstructors":[41],"on":[42,58],"paired":[43],"real":[44],"event":[45],"data.":[46],"On":[47,132],"192":[48,135],"windows":[49],"from":[50,180,189],"four":[51],"held-out":[52],"DSEC":[53],"recordings,":[54],"thresholds":[55],"calibrated":[56],"only":[57],"development":[59],"data":[60],"yielded":[61,85,101,138],"40":[62,102],"of":[63,69,165,195],"52":[64],"person":[65],"detections":[66,87,103,122],"12":[68],"140":[70],"background":[71,90,126,156],"positives":[72,127],"with":[73,123,141,146],"E2VID.":[74],"The":[75,174],"strongest":[76],"encoding":[79],"at":[80,95,107,128],"an":[81],"admissible":[82],"fixed":[83],"cutoff":[84,172],"14":[86],"seven":[89],"positives.":[91],"A":[92,158],"retrospective":[93],"comparison":[94],"common":[97],"10%":[98],"background-positive":[99],"cap":[100],"for":[104,110],"E2VID":[105,142],"most":[108],"19":[109],"any":[111],"encoding.":[113],"FireNet+":[114],"had":[115],"lower":[116],"preparation":[117],"cost":[118,191],"produced":[120],"21":[121],"zero":[124],"observed":[125],"its":[129],"frozen":[130],"cutoff.":[131],"same":[134],"windows,":[136],"Qwen3-VL-4B":[137],"33/52,":[139],"2/140":[140],"9/52,":[144],"9/140":[145],"highest-recall":[148],"rendering,":[150],"three-bin":[152],"image":[153],"(person":[154],"detections,":[155],"positives).":[157],"96-second":[159],"exploratory":[160],"replay":[161],"illustrates":[162],"sensitivity":[164],"episode":[166],"capture":[167],"starts":[170,194],"to":[171],"selection.":[173],"experiments":[175],"identify":[176],"detection":[178],"benefit":[179],"reconstruction":[181],"under":[182],"controlled":[183],"input":[184],"history":[185],"distinguish":[187],"it":[188],"false":[193],"continuous":[196],"triggering.":[197]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
