{"id":"https://openalex.org/W3154525391","doi":"https://doi.org/10.1145/3447450.3447477","title":"Research of single object tracking method based on Siamese Network and Level Set","display_name":"Research of single object tracking method based on Siamese Network and Level Set","publication_year":2020,"publication_date":"2020-12-25","ids":{"openalex":"https://openalex.org/W3154525391","doi":"https://doi.org/10.1145/3447450.3447477","mag":"3154525391"},"language":"en","primary_location":{"id":"doi:10.1145/3447450.3447477","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447450.3447477","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 The 4th International Conference on Video and Image Processing","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/A5101695186","display_name":"Tianbo Liu","orcid":"https://orcid.org/0000-0001-8309-3692"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianbo Liu","raw_affiliation_strings":["Shenyang Jianzhu University, China"],"affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102005647","display_name":"Li Su","orcid":"https://orcid.org/0000-0002-0991-8809"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Su","raw_affiliation_strings":["Shenyang Jianzhu University, China"],"affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100777115","display_name":"Shuai Yuan","orcid":"https://orcid.org/0000-0002-8709-7292"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Yuan","raw_affiliation_strings":["Shenyang Jianzhu University, China"],"affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080476856","display_name":"Gong Cheng","orcid":"https://orcid.org/0000-0001-5030-0683"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gong Cheng","raw_affiliation_strings":["Shenyang Jianzhu University, China"],"affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101916535","display_name":"Feng Zhang","orcid":"https://orcid.org/0000-0003-0760-7983"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhang","raw_affiliation_strings":["Shenyang Jianzhu University, China"],"affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University, China","institution_ids":["https://openalex.org/I83714178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101695186"],"corresponding_institution_ids":["https://openalex.org/I83714178"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.19274719,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"168","last_page":"173"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9829999804496765,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9811999797821045,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7682336568832397},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7169249653816223},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6734712719917297},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6606473922729492},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.6511906385421753},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6313417553901672},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5727108716964722},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5650236010551453},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5409058928489685},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5175182819366455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5152909755706787},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5075129270553589},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48623552918434143},{"id":"https://openalex.org/keywords/level-set","display_name":"Level set (data structures)","score":0.47018635272979736},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.43436312675476074},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4061068892478943}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7682336568832397},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7169249653816223},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6734712719917297},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6606473922729492},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.6511906385421753},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6313417553901672},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5727108716964722},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5650236010551453},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5409058928489685},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5175182819366455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5152909755706787},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5075129270553589},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48623552918434143},{"id":"https://openalex.org/C153008295","wikidata":"https://www.wikidata.org/wiki/Q6535093","display_name":"Level set (data structures)","level":2,"score":0.47018635272979736},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.43436312675476074},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4061068892478943},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447450.3447477","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447450.3447477","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 The 4th International Conference on Video and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G35658185","display_name":null,"funder_award_id":"62073227","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":11,"referenced_works":["https://openalex.org/W161114242","https://openalex.org/W1997121481","https://openalex.org/W2408241409","https://openalex.org/W2557641257","https://openalex.org/W2604272474","https://openalex.org/W2886910176","https://openalex.org/W2888456413","https://openalex.org/W2963227409","https://openalex.org/W2964423614","https://openalex.org/W3035211844","https://openalex.org/W4246999471"],"related_works":["https://openalex.org/W2102683994","https://openalex.org/W2965594636","https://openalex.org/W2063813108","https://openalex.org/W2373659438","https://openalex.org/W2162755489","https://openalex.org/W1879755808","https://openalex.org/W2072057318","https://openalex.org/W2164781768","https://openalex.org/W2050783914","https://openalex.org/W2085099126"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,12],"single":[6],"object":[7,23,43,62],"tracking":[8,24,63],"method":[9],"based":[10,56],"on":[11,41,57,68,94],"Siamese":[13,53],"network":[14,55],"and":[15,25,36,46,86,92,97,112],"level":[16,74],"set":[17,75],"to":[18],"increase":[19],"the":[20,42,47,52,60,69,73,78,82,87,95,105,109,114,117],"accuracy":[21,118],"of":[22,32,81],"segmentation":[26],"while":[27],"maintaining":[28],"real-time":[29,110],"tracking.":[30],"First":[31],"all,":[33],"feature":[34,70],"extraction":[35],"similarity":[37],"measurement":[38],"are":[39],"performed":[40,67],"template":[44],"image":[45,49],"search":[48],"by":[50],"using":[51,72],"backbone":[54],"ResNet.":[58],"Then":[59],"top-down":[61],"contour":[64],"refinement":[65],"is":[66,84,90],"map":[71],"method.":[76],"Finally,":[77],"loss":[79],"function":[80],"algorithm":[83,89,107],"defined,":[85],"proposed":[88,106],"evaluated":[91],"compared":[93],"VOT":[96],"DAVIS":[98],"dataset.":[99],"The":[100],"experimental":[101],"results":[102],"illustrate":[103],"that":[104],"maintains":[108],"performance,":[111],"at":[113],"same":[115],"time":[116],"has":[119],"been":[120],"significantly":[121],"improved.":[122]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}