{"id":"https://openalex.org/W2942913342","doi":"https://doi.org/10.1145/3297280.3297416","title":"Convolutional neural network with structural input for visual object tracking","display_name":"Convolutional neural network with structural input for visual object tracking","publication_year":2019,"publication_date":"2019-04-08","ids":{"openalex":"https://openalex.org/W2942913342","doi":"https://doi.org/10.1145/3297280.3297416","mag":"2942913342"},"language":"en","primary_location":{"id":"doi:10.1145/3297280.3297416","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297280.3297416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing","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/A5066756540","display_name":"Mustansar Fiaz","orcid":"https://orcid.org/0000-0003-2289-2284"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Mustansar Fiaz","raw_affiliation_strings":["Kyungpook National University, Daegu, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Kyungpook National University, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061017734","display_name":"Arif Mahmood","orcid":"https://orcid.org/0000-0001-5986-9876"},"institutions":[{"id":"https://openalex.org/I1323252656","display_name":"Information Technology University","ror":"https://ror.org/00ngv8j44","country_code":"PK","type":"education","lineage":["https://openalex.org/I1323252656"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Arif Mahmood","raw_affiliation_strings":["Information Technology University, Lahore, Pakistan"],"affiliations":[{"raw_affiliation_string":"Information Technology University, Lahore, Pakistan","institution_ids":["https://openalex.org/I1323252656"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077603212","display_name":"Soon Ki Jung","orcid":"https://orcid.org/0000-0003-0239-6785"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soon Ki Jung","raw_affiliation_strings":["Kyungpook National University, Daegu, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Kyungpook National University, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I31419693"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066756540"],"corresponding_institution_ids":["https://openalex.org/I31419693"],"apc_list":null,"apc_paid":null,"fwci":0.7086,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.74298767,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1345","last_page":"1352"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9840999841690063,"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":0.9660999774932861,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7877574563026428},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7766330242156982},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.7680432796478271},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6731184124946594},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6487612724304199},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5698928236961365},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5431591272354126},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5312544703483582},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.5107359886169434},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4615822434425354},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.43706172704696655},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43549472093582153},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42460817098617554},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.2954627275466919},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2009042501449585},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.09534159302711487}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7877574563026428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7766330242156982},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.7680432796478271},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6731184124946594},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6487612724304199},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5698928236961365},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5431591272354126},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5312544703483582},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.5107359886169434},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4615822434425354},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.43706172704696655},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43549472093582153},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42460817098617554},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2954627275466919},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2009042501449585},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09534159302711487},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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:10.1145/3297280.3297416","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297280.3297416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1424443765","https://openalex.org/W1554825167","https://openalex.org/W1686810756","https://openalex.org/W1745334888","https://openalex.org/W1857884451","https://openalex.org/W1915785815","https://openalex.org/W1955741794","https://openalex.org/W1964846093","https://openalex.org/W1984914017","https://openalex.org/W1991727946","https://openalex.org/W1995903777","https://openalex.org/W2001785244","https://openalex.org/W2009829488","https://openalex.org/W2038943746","https://openalex.org/W2102605133","https://openalex.org/W2109579504","https://openalex.org/W2113325037","https://openalex.org/W2113577207","https://openalex.org/W2121193292","https://openalex.org/W2124211486","https://openalex.org/W2124386111","https://openalex.org/W2126302311","https://openalex.org/W2145287260","https://openalex.org/W2154889144","https://openalex.org/W2158592639","https://openalex.org/W2161969291","https://openalex.org/W2162152641","https://openalex.org/W2162919312","https://openalex.org/W2165037244","https://openalex.org/W2168356304","https://openalex.org/W2214352687","https://openalex.org/W2273376582","https://openalex.org/W2280226538","https://openalex.org/W2309235535","https://openalex.org/W2343187456","https://openalex.org/W2346595613","https://openalex.org/W2408241409","https://openalex.org/W2470394683","https://openalex.org/W2473868734","https://openalex.org/W2474941434","https://openalex.org/W2513005088","https://openalex.org/W2520013149","https://openalex.org/W2558899534","https://openalex.org/W2592266547","https://openalex.org/W2618530766","https://openalex.org/W2735025962","https://openalex.org/W2737572441","https://openalex.org/W2748261474","https://openalex.org/W2776035257","https://openalex.org/W2786146995","https://openalex.org/W2944735799","https://openalex.org/W2953031486","https://openalex.org/W2964138017","https://openalex.org/W2964253307","https://openalex.org/W3098217967","https://openalex.org/W3098331309","https://openalex.org/W3102624093","https://openalex.org/W3123635396"],"related_works":["https://openalex.org/W2130674020","https://openalex.org/W2093748878","https://openalex.org/W2333771223","https://openalex.org/W2120056845","https://openalex.org/W1981531423","https://openalex.org/W4394861761","https://openalex.org/W1977371217","https://openalex.org/W2035264131","https://openalex.org/W1679012645","https://openalex.org/W1925461966"],"abstract_inverted_index":{"Numerous":[0],"deep":[1,60],"learning":[2,32,124],"approaches":[3,26],"have":[4,27,140],"been":[5,141],"applied":[6],"to":[7,12,15,31,92,115,132],"visual":[8],"object":[9],"tracking":[10,135,161,165],"owing":[11],"their":[13],"capabilities":[14],"leverage":[16],"huge":[17],"training":[18],"data":[19],"for":[20],"performance":[21],"improvement.":[22],"Most":[23],"of":[24,46,69,97],"these":[25],"limitations":[28],"with":[29,150],"regard":[30],"target":[33],"specific":[34],"information":[35],"rich":[36],"features":[37],"and":[38,55,73,78,90,105,120,129,145,180],"therefore":[39],"observe":[40],"reduced":[41],"accuracy":[42,170],"in":[43,66,82,171],"the":[44,67,76,79,98,107,111,134,155],"presence":[45],"different":[47,172],"challenges":[48,173],"such":[49],"as":[50],"occlusion,":[51,175],"scale":[52,181],"variations,":[53],"rotation":[54,179],"clutter.":[56],"We":[57],"proposed":[58,101,156,164],"a":[59,93],"neural":[61],"network":[62,102],"that":[63,154],"takes":[64],"input":[65,96,113],"form":[68],"two":[70,112],"stacked":[71],"patches":[72,86,114],"regresses":[74],"both":[75],"similarity":[77,119],"dis-similarity":[80],"scores":[81],"single":[83],"evaluation.":[84],"Image":[85],"are":[87],"concatenated":[88],"depth-wise":[89],"fed":[91],"six":[94],"channel":[95],"network.":[99],"The":[100,163],"is":[103,125],"generic":[104],"exploits":[106],"structural":[108],"differences":[109],"between":[110],"obtain":[116],"more":[117],"accurate":[118],"dissimilarity":[121],"scores.":[122],"Online":[123],"enforced":[126],"via":[127],"short-term":[128],"long-term":[130],"updates":[131],"improve":[133],"performance.":[136,162],"Extensive":[137],"experimental":[138],"evaluations":[139],"performed":[142],"on":[143],"OTB2015":[144],"TempleColor128":[146],"benchmark":[147],"datasets.":[148],"Comparisons":[149],"state-of-the-art":[151],"methods":[152],"indicate":[153],"framework":[157,166],"has":[158,167],"achieved":[159],"better":[160],"obtained":[168],"improved":[169],"including":[174],"background":[176],"clutter,":[177],"in-plane":[178],"variations.":[182]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}