{"id":"https://openalex.org/W2512936217","doi":"https://doi.org/10.1109/icip.2016.7532341","title":"Keypoint trajectory coding on compact descriptor for video analysis","display_name":"Keypoint trajectory coding on compact descriptor for video analysis","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2512936217","doi":"https://doi.org/10.1109/icip.2016.7532341","mag":"2512936217"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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/A5016854114","display_name":"Dong Tian","orcid":"https://orcid.org/0000-0002-2310-0974"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dong Tian","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL),Cambridge,Massachusetts,USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL),Cambridge,Massachusetts,USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103071044","display_name":"Huifang Sun","orcid":"https://orcid.org/0000-0003-2918-1522"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huifang Sun","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL),Cambridge,Massachusetts,USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL),Cambridge,Massachusetts,USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049841606","display_name":"Anthony Vetro","orcid":"https://orcid.org/0000-0002-8194-573X"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anthony Vetro","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL),Cambridge,Massachusetts,USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL),Cambridge,Massachusetts,USA","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5016854114"],"corresponding_institution_ids":["https://openalex.org/I4210159266"],"apc_list":null,"apc_paid":null,"fwci":0.501,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72388273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"171","last_page":"175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9997000098228455,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9995999932289124,"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/computer-science","display_name":"Computer science","score":0.684440553188324},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.6534885168075562},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6513866782188416},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.580798864364624},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5368779897689819},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18566471338272095},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09608235955238342},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06488907337188721}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.684440553188324},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.6534885168075562},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6513866782188416},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.580798864364624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5368779897689819},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18566471338272095},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09608235955238342},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06488907337188721},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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":14,"referenced_works":["https://openalex.org/W914561379","https://openalex.org/W1677409904","https://openalex.org/W1913356549","https://openalex.org/W2020701927","https://openalex.org/W2052311585","https://openalex.org/W2069178104","https://openalex.org/W2115579991","https://openalex.org/W2120419212","https://openalex.org/W2124088136","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2293559285","https://openalex.org/W6624433688","https://openalex.org/W6683411478"],"related_works":["https://openalex.org/W2755342338","https://openalex.org/W1891287906","https://openalex.org/W2036807459","https://openalex.org/W2775347418","https://openalex.org/W1969923398","https://openalex.org/W2772917594","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2058170566"],"abstract_inverted_index":{"In":[0,15,83],"contrast":[1],"to":[2,12,58,77,93,119,141,150],"still":[3],"image":[4],"analysis,":[5],"motion":[6,17],"information":[7,57],"offers":[8],"a":[9,30,74,96,100,109,116,135,151],"powerful":[10],"means":[11],"analyze":[13],"video.":[14],"particular,":[16,84],"trajectories":[18,114],"determined":[19],"from":[20],"keypoints":[21],"have":[22],"become":[23],"very":[24],"popular":[25,127],"in":[26,95,115],"recent":[27],"years":[28],"for":[29,108],"variety":[31],"of":[32,44,54,63,111,139],"video":[33,56,64,117],"analysis":[34,43,62],"tasks,":[35],"including":[36,129],"search,":[37],"retrieval":[38],"and":[39,132],"classification.":[40],"Additionally,":[41,99],"cloud-based":[42],"media":[45],"content":[46],"has":[47],"been":[48],"gaining":[49],"momentum,":[50],"so":[51],"efficient":[52],"communication":[53],"salient":[55],"perform":[59],"the":[60,66,80,91,112],"necessary":[61],"at":[65],"cloud":[67],"server":[68],"is":[69,88,104],"needed.":[70],"This":[71],"paper":[72],"describes":[73],"novel":[75],"framework":[76],"efficiently":[78],"represent":[79],"keypoint":[81],"trajectories.":[82],"an":[85],"interframe":[86],"prediction":[87],"designed":[89],"with":[90,143],"option":[92],"operate":[94],"low-delay":[97],"mode.":[98],"scalable":[101],"coding":[102,147],"method":[103],"proposed":[105,145],"that":[106],"allows":[107],"subset":[110],"coded":[113],"segment":[118],"be":[120],"easily":[121],"accessed.":[122],"Experimental":[123],"results":[124],"on":[125],"several":[126],"datasets":[128],"Stanford":[130],"MAR":[131],"Hopkin155":[133],"demonstrate":[134],"significant":[136],"rate":[137],"saving":[138],"up":[140],"25%":[142],"our":[144],"trajectory":[146],"approaches":[148],"relative":[149],"state-of-the-art":[152],"reference":[153],"approach.":[154]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
