{"id":"https://openalex.org/W4396601654","doi":"https://doi.org/10.1145/3645259.3645268","title":"Real-Time Predictability Analysis and Enhancement of Deep-Learning-Based Object Tracking","display_name":"Real-Time Predictability Analysis and Enhancement of Deep-Learning-Based Object Tracking","publication_year":2024,"publication_date":"2024-01-12","ids":{"openalex":"https://openalex.org/W4396601654","doi":"https://doi.org/10.1145/3645259.3645268"},"language":"en","primary_location":{"id":"doi:10.1145/3645259.3645268","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3645259.3645268","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 6th International Conference on Image Processing and Machine Vision","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/A5046174212","display_name":"Mingyang Zhang","orcid":"https://orcid.org/0000-0002-8530-6257"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Mingyang Zhang","raw_affiliation_strings":["PSI-EAVISE research group, Department of Electrical Engineering, KU Leuven, Belgium"],"raw_orcid":"https://orcid.org/0000-0002-8530-6257","affiliations":[{"raw_affiliation_string":"PSI-EAVISE research group, Department of Electrical Engineering, KU Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085816625","display_name":"Kristof Van Beeck","orcid":"https://orcid.org/0000-0002-3667-7406"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Kristof Van Beeck","raw_affiliation_strings":["PSI-EAVISE research group, Department of Electrical Engineering, KU Leuven, Belgium"],"raw_orcid":"https://orcid.org/0000-0002-3667-7406","affiliations":[{"raw_affiliation_string":"PSI-EAVISE research group, Department of Electrical Engineering, KU Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057957125","display_name":"Toon Goedem\u00e9","orcid":"https://orcid.org/0000-0002-7477-8961"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Toon Goedem\u00e9","raw_affiliation_strings":["PSI-EAVISE research group, Department of Electrical Engineering, KU Leuven, Belgium"],"raw_orcid":"https://orcid.org/0000-0002-7477-8961","affiliations":[{"raw_affiliation_string":"PSI-EAVISE research group, Department of Electrical Engineering, KU Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99464096"],"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":"48","last_page":"52"},"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/T12222","display_name":"IoT-based Smart Home Systems","score":0.9772999882698059,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9735000133514404,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/predictability","display_name":"Predictability","score":0.8238700032234192},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.804822564125061},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.6482658386230469},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.608537495136261},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5655518174171448},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5179397463798523},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5162968039512634},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.4823717176914215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4632505178451538},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4606606364250183},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4126228392124176},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.38599875569343567},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.37636837363243103}],"concepts":[{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.8238700032234192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.804822564125061},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.6482658386230469},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.608537495136261},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5655518174171448},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5179397463798523},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5162968039512634},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.4823717176914215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4632505178451538},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4606606364250183},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4126228392124176},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.38599875569343567},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.37636837363243103},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3645259.3645268","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3645259.3645268","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 6th International Conference on Image Processing and Machine Vision","raw_type":"proceedings-article"},{"id":"pmh:oai:lirias2repo.kuleuven.be:20.500.12942/754724","is_oa":false,"landing_page_url":"https://lirias.kuleuven.be/handle/20.500.12942/754724","pdf_url":null,"source":{"id":"https://openalex.org/S7407055369","display_name":"Lirias","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"6th International Conference on Image Processing and Machine Vision (IPMV), PEOPLES R CHINA, Macau, 12-14 January 2024","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2799058067","https://openalex.org/W2912891814","https://openalex.org/W2963534981","https://openalex.org/W3008905965","https://openalex.org/W3011043778","https://openalex.org/W3033653670","https://openalex.org/W3090916454","https://openalex.org/W4367156011"],"related_works":["https://openalex.org/W2726467123","https://openalex.org/W2064726690","https://openalex.org/W4252678288","https://openalex.org/W4254065731","https://openalex.org/W1607297154","https://openalex.org/W4210820789","https://openalex.org/W4237782192","https://openalex.org/W2913177154","https://openalex.org/W4235131201","https://openalex.org/W4232793539"],"abstract_inverted_index":{"While":[0],"Siamese":[1],"object":[2,61,63,137,152],"tracking":[3,64,138],"has":[4],"witnessed":[5],"significant":[6],"advancements,":[7],"its":[8],"hard":[9,78],"real-time":[10,53,79,136],"behaviour":[11],"on":[12,112,116,149],"embedded":[13,23,113],"devices":[14],"remains":[15],"inadequately":[16],"addressed.":[17],"In":[18],"many":[19],"application":[20],"cases,":[21],"an":[22],"implementation":[24],"should":[25,36],"not":[26],"only":[27],"have":[28],"a":[29,58,89,117,141],"minimal":[30],"execution":[31],"latency,":[32],"but":[33,81],"this":[34,47],"latency":[35],"ideally":[37],"also":[38],"be":[39],"having":[40],"zero":[41],"variance,":[42],"i.e.":[43],"predictable.":[44],"To":[45],"bridge":[46],"gap,":[48],"we":[49,87],"firstly":[50],"analyse":[51],"the":[52,70,95,107,129],"predictability":[54],"of":[55,57,72,77,132],"components":[56],"state-of-the-art":[59],"deep-learning-based":[60],"video":[62],"system.":[65],"Our":[66,97],"detailed":[67],"experiments":[68],"indicate":[69],"superiority":[71],"FPGA":[73],"implementations":[74],"in":[75,135],"terms":[76],"behaviour,":[80],"unveil":[82],"important":[83],"time-predictability":[84,111],"bottlenecks.":[85],"Then,":[86],"craft":[88],"dedicated":[90],"hardware":[91,133],"accelerator":[92],"specifically":[93],"for":[94,143],"bottleneck.":[96],"method":[98],"seamlessly":[99],"integrates":[100],"advanced":[101],"tracker":[102,122],"features":[103],"and":[104,110,139],"improves":[105],"greatly":[106],"tracker\u2019s":[108],"speed":[109],"systems.":[114],"Implemented":[115],"KV260":[118],"board,":[119],"our":[120],"quantized":[121],"demonstrates":[123],"superior":[124],"performance.":[125],"These":[126],"findings":[127],"spotlight":[128],"immense":[130],"promise":[131],"acceleration":[134],"set":[140],"benchmark":[142],"forthcoming":[144],"hardware-software":[145],"co-design":[146],"pursuits":[147],"focused":[148],"achieving":[150],"time-predictable":[151],"tracking.":[153]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}