{"id":"https://openalex.org/W3086871928","doi":"https://doi.org/10.1145/3408127.3408167","title":"Low-latency Block-wise Object Detection Method using SSD for High Resolution Video","display_name":"Low-latency Block-wise Object Detection Method using SSD for High Resolution Video","publication_year":2020,"publication_date":"2020-06-19","ids":{"openalex":"https://openalex.org/W3086871928","doi":"https://doi.org/10.1145/3408127.3408167","mag":"3086871928"},"language":"en","primary_location":{"id":"doi:10.1145/3408127.3408167","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3408127.3408167","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3408127.3408167","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 4th International Conference on Digital Signal Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3408127.3408167","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073959816","display_name":"Kazuki Hozumi","orcid":null},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kazuki Hozumi","raw_affiliation_strings":["School of Computer Science and Engineering, University of Aizu, Fukushima, Japan"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Aizu, Fukushima, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101768297","display_name":"Yoichi Tomioka","orcid":"https://orcid.org/0000-0003-3509-6607"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoichi Tomioka","raw_affiliation_strings":["School of Computer Science and Engineering, University of Aizu, Fukushima, Japan"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Aizu, Fukushima, Japan","institution_ids":["https://openalex.org/I141591182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5073959816"],"corresponding_institution_ids":["https://openalex.org/I141591182"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09860859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"61","last_page":"65"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.996999979019165,"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":0.996999979019165,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9930999875068665,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/computer-science","display_name":"Computer science","score":0.8361765742301941},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7080475687980652},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6809289455413818},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.6477853655815125},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.6014952659606934},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6005532741546631},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5838502049446106},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.5012273788452148},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4761464595794678},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.3502764105796814},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.28200286626815796}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8361765742301941},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7080475687980652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6809289455413818},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.6477853655815125},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.6014952659606934},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6005532741546631},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5838502049446106},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.5012273788452148},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4761464595794678},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3502764105796814},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28200286626815796},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3408127.3408167","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3408127.3408167","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3408127.3408167","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 4th International Conference on Digital Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3408127.3408167","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3408127.3408167","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3408127.3408167","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 4th International Conference on Digital Signal Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321680","display_name":"New Energy and Industrial Technology Development Organization","ror":"https://ror.org/0055k7a87"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3086871928.pdf","grobid_xml":"https://content.openalex.org/works/W3086871928.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1483870316","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W2031489346","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2117539524","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2291627510","https://openalex.org/W2604272474","https://openalex.org/W2612445135","https://openalex.org/W2899607431","https://openalex.org/W2949658958","https://openalex.org/W2963037989","https://openalex.org/W3106250896","https://openalex.org/W6682348150","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4287991909","https://openalex.org/W4390721878","https://openalex.org/W2004851054","https://openalex.org/W2965594636","https://openalex.org/W1875422203","https://openalex.org/W4310880131","https://openalex.org/W2912550626","https://openalex.org/W3216382677","https://openalex.org/W2810129309"],"abstract_inverted_index":{"In":[0,112,148],"recent":[1],"years,":[2],"in":[3],"the":[4,64,100,137,143,149,151,163,171,179,188],"fields":[5],"such":[6,19],"as":[7,20],"surveillance":[8],"cameras":[9],"and":[10,45,141,155],"in-vehicle":[11],"camera":[12],"systems,":[13],"efficient":[14],"deep-learning-based":[15],"object":[16,103,120,152,174],"detection":[17,104,121,153,183],"methods,":[18],"Single":[21],"Shot":[22],"MultiBox":[23],"Detector":[24],"(SSD),":[25],"that":[26],"do":[27],"not":[28],"require":[29,40],"window":[30],"scanning":[31],"have":[32],"received":[33],"a":[34,41,117,125],"significant":[35],"attention.":[36],"However,":[37,97],"these":[38],"methods":[39],"lot":[42],"of":[43,78,102,145,173,181],"memory":[44,76],"computation.":[46],"For":[47],"this":[48,113],"reason,":[49],"when":[50],"we":[51,90,106,115,177],"applying":[52],"them":[53],"to":[54,62,73,87,108,193],"higher":[55],"definition":[56],"video,":[57],"it":[58,98],"can":[59,91],"be":[60],"necessary":[61],"divide":[63],"video":[65],"into":[66,129],"multiple":[67,146],"blocks":[68],"for":[69],"inference":[70],"processing":[71],"due":[72,86],"restrictions":[74],"on":[75,136],"capacity":[77],"GPUs":[79],"or":[80],"FPGAs.":[81],"To":[82],"avoid":[83],"accuracy":[84,154,189],"degeneration":[85,190],"block":[88,95,127,138],"division,":[89],"use":[92],"conventional":[93],"overlapping":[94],"technique.":[96],"increases":[99],"latency":[101,156,180],"because":[105],"need":[107],"process":[109],"more":[110],"blocks.":[111],"paper,":[114],"propose":[116],"low-latency":[118],"block-wise":[119],"method":[122],"which":[123],"assigns":[124],"different":[126],"pattern":[128,139],"each":[130,133],"frame,":[131],"divides":[132],"frame":[134],"based":[135],"assignment,":[140],"integrates":[142],"results":[144],"frames.":[147],"experiments,":[150],"were":[157],"evaluated":[158],"using":[159],"three":[160],"data":[161],"from":[162],"Multiple":[164],"Object":[165],"Tracking":[166],"Benchmark":[167],"dataset":[168],"2017.":[169],"When":[170],"movement":[172],"is":[175,191],"small,":[176],"reduced":[178],"human":[182],"by":[184],"about":[185],"40%":[186],"while":[187],"0%":[192],"2%.":[194]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}