{"id":"https://openalex.org/W4392903624","doi":"https://doi.org/10.1109/icassp48485.2024.10446257","title":"Trades++: Enhancing Multi-Object Tracking of Real Low Confidence Targets Using a Pyramid-Like Self-Attention Model","display_name":"Trades++: Enhancing Multi-Object Tracking of Real Low Confidence Targets Using a Pyramid-Like Self-Attention Model","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392903624","doi":"https://doi.org/10.1109/icassp48485.2024.10446257"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10446257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10446257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087746635","display_name":"Chenxin Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenxin Wen","raw_affiliation_strings":["Xidian University,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100625179","display_name":"Yan Gao","orcid":"https://orcid.org/0009-0006-6216-3165"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Gao","raw_affiliation_strings":["Xidian University,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102011570","display_name":"Jie Li","orcid":"https://orcid.org/0000-0003-3102-6425"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Li","raw_affiliation_strings":["Xidian University,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0314132,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3465","last_page":"3469"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7738643884658813},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7214245796203613},{"id":"https://openalex.org/keywords/bittorrent-tracker","display_name":"BitTorrent tracker","score":0.6425439715385437},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6320841312408447},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.6039516925811768},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6025643348693848},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.584538459777832},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5362892150878906},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5203014016151428},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5158640742301941},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4938560128211975},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.47846078872680664},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.47157472372055054},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.46006572246551514},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3439425230026245},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2964065968990326},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.2532666325569153},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.1880275011062622},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10529336333274841},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10163021087646484}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7738643884658813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7214245796203613},{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.6425439715385437},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6320841312408447},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.6039516925811768},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6025643348693848},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.584538459777832},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5362892150878906},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5203014016151428},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5158640742301941},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4938560128211975},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.47846078872680664},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.47157472372055054},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.46006572246551514},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3439425230026245},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2964065968990326},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.2532666325569153},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.1880275011062622},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10529336333274841},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10163021087646484},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10446257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10446257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2022121445","display_name":null,"funder_award_id":"U21A20514","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3453920714","display_name":null,"funder_award_id":"62221005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4297946667","display_name":null,"funder_award_id":"U22A2096","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8946150788","display_name":null,"funder_award_id":"62176195","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G913836228","display_name":null,"funder_award_id":"62036007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2252355370","https://openalex.org/W2962766617","https://openalex.org/W2963323244","https://openalex.org/W2983208726","https://openalex.org/W3035060563","https://openalex.org/W3035442500","https://openalex.org/W3035727180","https://openalex.org/W3084173793","https://openalex.org/W3095753995","https://openalex.org/W3104218139","https://openalex.org/W3106546328","https://openalex.org/W3106763294","https://openalex.org/W3165926952","https://openalex.org/W3167949052","https://openalex.org/W3177052299","https://openalex.org/W3207282964","https://openalex.org/W4205537101","https://openalex.org/W4214516362","https://openalex.org/W4286904999","https://openalex.org/W4372346401","https://openalex.org/W4372349741","https://openalex.org/W4386071841"],"related_works":["https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2348780717","https://openalex.org/W2012200063","https://openalex.org/W2922437833","https://openalex.org/W4320086059","https://openalex.org/W4210734798","https://openalex.org/W4223892596","https://openalex.org/W4312696271"],"abstract_inverted_index":{"In":[0,53],"reality,":[1],"multi-object":[2,33],"tracking":[3,29],"(MOT)":[4],"is":[5,25,91],"used":[6],"in":[7,21],"a":[8,58,69,81,100],"wide":[9],"range":[10],"of":[11,17,31,83,120],"scenarios.":[12],"Maintaining":[13],"the":[14,18,38,45,51,65,75,85,95,118,132],"motion":[15],"trajectory":[16],"target,":[19],"especially":[20,122],"high-density":[22],"pedestrian":[23],"scenarios,":[24],"often":[26,43],"difficult.":[27],"The":[28,88],"quality":[30,40],"most":[32],"trackers":[34],"correlates":[35],"strongly":[36],"with":[37,104],"detector":[39],"and":[41,79,125,135],"they":[42],"ignore":[44],"low-scoring":[46],"detection":[47,66],"boxes":[48],"obtained":[49],"by":[50],"detector.":[52],"this":[54],"paper,":[55],"we":[56],"propose":[57],"TraDeS-based":[59],"method":[60],"called":[61],"TraDeS++":[62],"that":[63,113],"enhances":[64],"features":[67],"using":[68],"pyramid-like":[70],"self-attention":[71],"model,":[72],"significantly":[73],"reducing":[74],"model":[76],"training":[77,86],"time":[78],"achieving":[80],"reduction":[82],"half":[84],"epochs.":[87],"second":[89],"motivation":[90],"to":[92],"focus":[93],"on":[94,131],"association":[96],"method.":[97],"We":[98],"use":[99],"two-stage":[101],"matching":[102],"strategy":[103],"GIoU":[105],"constraints,":[106],"effectively":[107,116],"improving":[108],"HOTA.":[109],"Experimental":[110],"results":[111,128],"show":[112],"our":[114],"component":[115],"improves":[117],"metrics":[119],"MOT,":[121],"MOTA,":[123],"HOTA,":[124],"IDF1.":[126],"Competitive":[127],"are":[129],"achieved":[130],"popular":[133],"MOT16":[134],"MOT17":[136],"datasets.":[137]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
