{"id":"https://openalex.org/W2789774226","doi":"https://doi.org/10.1109/itsc.2017.8317904","title":"A survey on leveraging deep neural networks for object tracking","display_name":"A survey on leveraging deep neural networks for object tracking","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2789774226","doi":"https://doi.org/10.1109/itsc.2017.8317904","mag":"2789774226"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2017.8317904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2017.8317904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"article","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/A5002815046","display_name":"Sebastian Krebs","orcid":"https://orcid.org/0000-0002-2740-6719"},"institutions":[{"id":"https://openalex.org/I891521709","display_name":"Daimler (Germany)","ror":"https://ror.org/00m0j3d84","country_code":"DE","type":"company","lineage":["https://openalex.org/I891521709"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Sebastian Krebs","raw_affiliation_strings":["Daimler AG Research and Development, Ulm, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Daimler AG Research and Development, Ulm, Germany","institution_ids":["https://openalex.org/I891521709"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046915434","display_name":"Bharanidhar Duraisamy","orcid":null},"institutions":[{"id":"https://openalex.org/I891521709","display_name":"Daimler (Germany)","ror":"https://ror.org/00m0j3d84","country_code":"DE","type":"company","lineage":["https://openalex.org/I891521709"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bharanidhar Duraisamy","raw_affiliation_strings":["Daimler AG Research and Development, Ulm, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Daimler AG Research and Development, Ulm, Germany","institution_ids":["https://openalex.org/I891521709"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007686963","display_name":"Fabian B. Flohr","orcid":"https://orcid.org/0000-0002-1499-3790"},"institutions":[{"id":"https://openalex.org/I891521709","display_name":"Daimler (Germany)","ror":"https://ror.org/00m0j3d84","country_code":"DE","type":"company","lineage":["https://openalex.org/I891521709"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabian Flohr","raw_affiliation_strings":["Daimler AG Research and Development, Ulm, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Daimler AG Research and Development, Ulm, Germany","institution_ids":["https://openalex.org/I891521709"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002815046"],"corresponding_institution_ids":["https://openalex.org/I891521709"],"apc_list":null,"apc_paid":null,"fwci":1.4785,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.89835337,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"411","last_page":"418"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9983000159263611,"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"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer 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":0.779125452041626},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7685352563858032},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7451387643814087},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6385883688926697},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.6378802061080933},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.6052600741386414},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5870223045349121},{"id":"https://openalex.org/keywords/tracking-system","display_name":"Tracking system","score":0.5861434936523438},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5460975766181946},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5375027656555176},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5221540927886963},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5103947520256042},{"id":"https://openalex.org/keywords/radar-tracker","display_name":"Radar tracker","score":0.5033177733421326},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.4723697304725647},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4673521816730499},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33753907680511475},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.21973830461502075},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.14127963781356812},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13937464356422424},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.12611401081085205},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.11319956183433533}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.779125452041626},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7685352563858032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7451387643814087},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6385883688926697},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.6378802061080933},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.6052600741386414},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5870223045349121},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.5861434936523438},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5460975766181946},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5375027656555176},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5221540927886963},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5103947520256042},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.5033177733421326},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.4723697304725647},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4673521816730499},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33753907680511475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.21973830461502075},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.14127963781356812},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13937464356422424},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.12611401081085205},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.11319956183433533},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2017.8317904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2017.8317904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W216942251","https://openalex.org/W1497265063","https://openalex.org/W1515369433","https://openalex.org/W1531192956","https://openalex.org/W1673782286","https://openalex.org/W1686810756","https://openalex.org/W1854404533","https://openalex.org/W1857884451","https://openalex.org/W1909771825","https://openalex.org/W1984914017","https://openalex.org/W1995903777","https://openalex.org/W2048605796","https://openalex.org/W2055022211","https://openalex.org/W2064675550","https://openalex.org/W2076063813","https://openalex.org/W2105934661","https://openalex.org/W2108598243","https://openalex.org/W2118097920","https://openalex.org/W2118345389","https://openalex.org/W2126302311","https://openalex.org/W2130026429","https://openalex.org/W2132200263","https://openalex.org/W2138553450","https://openalex.org/W2140242774","https://openalex.org/W2150066425","https://openalex.org/W2150341604","https://openalex.org/W2155680787","https://openalex.org/W2158734944","https://openalex.org/W2163557299","https://openalex.org/W2163605009","https://openalex.org/W2167536584","https://openalex.org/W2186330282","https://openalex.org/W2211629196","https://openalex.org/W2214352687","https://openalex.org/W2222512263","https://openalex.org/W2263483072","https://openalex.org/W2264178313","https://openalex.org/W2289255337","https://openalex.org/W2291627510","https://openalex.org/W2337890890","https://openalex.org/W2340897893","https://openalex.org/W2342840547","https://openalex.org/W2408241409","https://openalex.org/W2424778531","https://openalex.org/W2491664569","https://openalex.org/W2518708963","https://openalex.org/W2527068611","https://openalex.org/W2562852216","https://openalex.org/W2567607488","https://openalex.org/W2919115771","https://openalex.org/W2962778460","https://openalex.org/W2963037989","https://openalex.org/W2963063317","https://openalex.org/W2963121817","https://openalex.org/W2963343478","https://openalex.org/W2964015640","https://openalex.org/W2964253307","https://openalex.org/W3106250896","https://openalex.org/W4205947740","https://openalex.org/W4211164258","https://openalex.org/W6630889139","https://openalex.org/W6638992375","https://openalex.org/W6677356763","https://openalex.org/W6677907805","https://openalex.org/W6679027886","https://openalex.org/W6688356126","https://openalex.org/W6703477647","https://openalex.org/W6703780166","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W1497101000","https://openalex.org/W35704881","https://openalex.org/W2146154945","https://openalex.org/W3021920309","https://openalex.org/W3177406559","https://openalex.org/W2766961550","https://openalex.org/W2789220062","https://openalex.org/W2186207697","https://openalex.org/W2086803433","https://openalex.org/W2054235656"],"abstract_inverted_index":{"Object":[0],"tracking":[1,31,50,124,140,165],"is":[2,108,156],"the":[3,9,71,103,111,119,129,150,162,168],"task":[4],"of":[5,11,29,73,105,122,149,153],"estimating":[6],"over":[7,33],"time":[8],"state":[10],"a":[12,62,113,126,146],"single":[13],"or":[14,24],"multiple":[15],"objects":[16],"based":[17,132],"on":[18,128,159],"noisy":[19],"measurements":[20],"received":[21],"from":[22,38],"one":[23],"several":[25,34],"sensors.":[26],"The":[27],"field":[28,121],"object":[30,100,123],"spans":[32],"application":[35],"domains":[36],"ranging":[37],"military":[39],"radar":[40],"systems":[41,141,155],"and":[42,56,89,134,145,172],"sensor":[43],"fusion":[44],"approaches,":[45],"to":[46,83,99,109],"today's":[47],"computer":[48],"vision":[49],"methods":[51,97],"employed":[52],"in":[53,65,79,87,167],"consumer":[54],"electronics":[55],"surveillance":[57],"systems.":[58],"It":[59],"also":[60,94],"plays":[61],"substantial":[63],"role":[64],"autonomous":[66],"driving.":[67],"In":[68],"recent":[69],"years,":[70],"use":[72],"deep":[74],"neural":[75],"networks":[76],"has":[77],"spiked":[78],"various":[80],"fields,":[81],"due":[82],"their":[84],"impressive":[85],"performance":[86],"detection":[88],"classification":[90],"tasks.":[91],"This":[92],"aspect":[93],"makes":[95],"these":[96],"applicable":[98],"tracking.":[101],"Therefore,":[102],"aim":[104],"this":[106,160],"survey":[107],"give":[110],"reader":[112],"brief":[114],"yet":[115],"comprehensive":[116],"start":[117],"into":[118],"widespread":[120],"with":[125],"focus":[127],"latest":[130],"deep-learning":[131],"extensions":[133],"approaches.":[135],"At":[136],"first,":[137],"traditional":[138],"non-deep":[139],"are":[142,170],"briefly":[143],"reviewed":[144],"generic":[147],"model":[148],"individual":[151],"components":[152],"such":[154],"introduced.":[157],"Based":[158],"structure":[161],"representative":[163],"deep-based":[164],"applications":[166],"literature":[169],"classified":[171],"presented.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}