{"id":"https://openalex.org/W2141681473","doi":"https://doi.org/10.1109/icdsc.2007.4357505","title":"Automatically Determining Dominant Motions in Crowded Scenes by Clustering Partial Feature Trajectories","display_name":"Automatically Determining Dominant Motions in Crowded Scenes by Clustering Partial Feature Trajectories","publication_year":2007,"publication_date":"2007-09-01","ids":{"openalex":"https://openalex.org/W2141681473","doi":"https://doi.org/10.1109/icdsc.2007.4357505","mag":"2141681473"},"language":"en","primary_location":{"id":"doi:10.1109/icdsc.2007.4357505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsc.2007.4357505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 First ACM/IEEE International Conference on Distributed Smart Cameras","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/A5068259837","display_name":"Anil Cheriyadat","orcid":null},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anil M. Cheriyadat","raw_affiliation_strings":["Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA","Rensselaer Polytech. Inst, Troy"],"affiliations":[{"raw_affiliation_string":"Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Rensselaer Polytech. Inst, Troy","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049207791","display_name":"Richard J. Radke","orcid":"https://orcid.org/0000-0001-5064-7775"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard J. Radke","raw_affiliation_strings":["Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA","Rensselaer Polytech. Inst, Troy"],"affiliations":[{"raw_affiliation_string":"Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Rensselaer Polytech. Inst, Troy","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068259837"],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":2.8978,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.91814724,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"12","issue":null,"first_page":"52","last_page":"58"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998000264167786,"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":0.9986000061035156,"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":0.7877582311630249},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7505456209182739},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.750126302242279},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.690373420715332},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.6432839632034302},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5611370801925659},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5373916029930115},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5084899663925171},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4974575340747833},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.44547346234321594},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42198002338409424},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.412492960691452},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2913007140159607},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1386081576347351},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.07459276914596558}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7877582311630249},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7505456209182739},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.750126302242279},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.690373420715332},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.6432839632034302},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5611370801925659},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5373916029930115},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5084899663925171},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4974575340747833},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.44547346234321594},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42198002338409424},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.412492960691452},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2913007140159607},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1386081576347351},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.07459276914596558},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icdsc.2007.4357505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsc.2007.4357505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 First ACM/IEEE International Conference on Distributed Smart Cameras","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.297.9892","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.9892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ecse.rpi.edu/~rjradke/papers/radke-icdsc07.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1532362218","https://openalex.org/W1864972570","https://openalex.org/W2008810234","https://openalex.org/W2034528422","https://openalex.org/W2040321327","https://openalex.org/W2046876370","https://openalex.org/W2049921702","https://openalex.org/W2076078388","https://openalex.org/W2096229530","https://openalex.org/W2109328551","https://openalex.org/W2118877769","https://openalex.org/W2125162832","https://openalex.org/W2132103241","https://openalex.org/W2135235396","https://openalex.org/W2140235142","https://openalex.org/W2144476963","https://openalex.org/W2146944178","https://openalex.org/W2161841955","https://openalex.org/W3040777582","https://openalex.org/W4236310464","https://openalex.org/W4238024369","https://openalex.org/W4241521373","https://openalex.org/W6631707545","https://openalex.org/W6639107604","https://openalex.org/W6677548441","https://openalex.org/W6780413204"],"related_works":["https://openalex.org/W2244560705","https://openalex.org/W1509820811","https://openalex.org/W346723641","https://openalex.org/W2116675934","https://openalex.org/W4307010368","https://openalex.org/W2789220062","https://openalex.org/W2732308154","https://openalex.org/W3084370450","https://openalex.org/W2422898642","https://openalex.org/W2534746541"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,10,63],"system":[3],"for":[4,66,105],"automatically":[5],"identifying":[6],"dominant":[7,60,87],"motions":[8,61,90],"in":[9,17,91],"crowded":[11,92],"scene.":[12],"Accurately":[13],"tracking":[14,37],"individual":[15],"objects":[16],"such":[18],"scenes":[19],"is":[20],"difficult":[21],"due":[22],"to":[23],"inter-and":[24],"intra-object":[25],"occlusions":[26],"that":[27,54,80],"cannot":[28],"be":[29,57,98],"easily":[30,99],"resolved.":[31],"Our":[32],"approach":[33,82],"begins":[34],"by":[35],"independently":[36],"low-level":[38],"features":[39],"using":[40,62],"optical":[41],"flow.":[42],"While":[43],"many":[44],"of":[45],"the":[46,81],"feature":[47,67],"point":[48],"tracks":[49],"are":[50],"unreliable,":[51],"we":[52],"show":[53],"they":[55],"can":[56,83],"clustered":[58],"into":[59,101],"distance":[64],"measure":[65],"trajectories":[68],"based":[69],"on":[70,75],"longest":[71],"common":[72],"subsequences.":[73],"Results":[74],"real":[76],"video":[77],"sequences":[78],"demonstrate":[79],"successfully":[84],"identify":[85],"both":[86],"and":[88],"anomalous":[89],"scenes.":[93],"These":[94],"fully-automatic":[95],"algorithms":[96],"could":[97],"incorporated":[100],"distributed":[102],"camera":[103],"networks":[104],"autonomous":[106],"scene":[107],"analysis.":[108]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}