{"id":"https://openalex.org/W4390225951","doi":"https://doi.org/10.1145/3631991.3632040","title":"Motion Segmentation of Pedestrian Trajectories Using Angular Gaussian Mixture Model","display_name":"Motion Segmentation of Pedestrian Trajectories Using Angular Gaussian Mixture Model","publication_year":2023,"publication_date":"2023-09-22","ids":{"openalex":"https://openalex.org/W4390225951","doi":"https://doi.org/10.1145/3631991.3632040"},"language":"en","primary_location":{"id":"doi:10.1145/3631991.3632040","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3631991.3632040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 The 5th World Symposium on Software Engineering (WSSE)","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/A5001551147","display_name":"Ahlam Al-Dhamari","orcid":"https://orcid.org/0000-0001-7595-4632"},"institutions":[{"id":"https://openalex.org/I31605765","display_name":"Hodeidah University","ror":"https://ror.org/05fkpm735","country_code":"YE","type":"education","lineage":["https://openalex.org/I31605765"]},{"id":"https://openalex.org/I4576418","display_name":"University of Technology Malaysia","ror":"https://ror.org/026w31v75","country_code":"MY","type":"education","lineage":["https://openalex.org/I4576418"]}],"countries":["MY","YE"],"is_corresponding":true,"raw_author_name":"Ahlam Al-Dhamari","raw_affiliation_strings":["Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Malaysia and \rFaculty of Computer Science and Engineering, Hodeidah University, Yemen"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Malaysia and \rFaculty of Computer Science and Engineering, Hodeidah University, Yemen","institution_ids":["https://openalex.org/I31605765","https://openalex.org/I4576418"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007511975","display_name":"Adel Hafeezallah","orcid":"https://orcid.org/0000-0002-0057-1706"},"institutions":[{"id":"https://openalex.org/I23075662","display_name":"Taibah University","ror":"https://ror.org/01xv1nn60","country_code":"SA","type":"education","lineage":["https://openalex.org/I23075662"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Adel Hafeezallah","raw_affiliation_strings":["Department of Electrical Engineering, Taibah University, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Taibah University, Saudi Arabia","institution_ids":["https://openalex.org/I23075662"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031515973","display_name":"S. A. R. Abu\u2013Bakar","orcid":"https://orcid.org/0000-0002-4360-6630"},"institutions":[{"id":"https://openalex.org/I4576418","display_name":"University of Technology Malaysia","ror":"https://ror.org/026w31v75","country_code":"MY","type":"education","lineage":["https://openalex.org/I4576418"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Syed Abd Rahman Abu-Bakar","raw_affiliation_strings":["Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Malaysia"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Malaysia","institution_ids":["https://openalex.org/I4576418"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5001551147"],"corresponding_institution_ids":["https://openalex.org/I31605765","https://openalex.org/I4576418"],"apc_list":null,"apc_paid":null,"fwci":0.123,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45315006,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"298","last_page":"304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9868999719619751,"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":0.7818844318389893},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7386942505836487},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7153601050376892},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6844786405563354},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5979375839233398},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5738274455070496},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.4631490111351013},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42379871010780334},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.42086246609687805},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.41278788447380066},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.398695170879364},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11253100633621216}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7818844318389893},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7386942505836487},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7153601050376892},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6844786405563354},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5979375839233398},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5738274455070496},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.4631490111351013},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42379871010780334},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.42086246609687805},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.41278788447380066},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.398695170879364},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11253100633621216},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3631991.3632040","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3631991.3632040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 The 5th World Symposium on Software Engineering (WSSE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2036721747","https://openalex.org/W2046082958","https://openalex.org/W2049633694","https://openalex.org/W2108404684","https://openalex.org/W2121839066","https://openalex.org/W2138835141","https://openalex.org/W2150593711","https://openalex.org/W2154946129","https://openalex.org/W2325880033","https://openalex.org/W2551437525","https://openalex.org/W2773129881","https://openalex.org/W2805447019","https://openalex.org/W2894692710","https://openalex.org/W2897819140","https://openalex.org/W3004523911","https://openalex.org/W3013865762","https://openalex.org/W3048650059","https://openalex.org/W3126955194","https://openalex.org/W4210353804","https://openalex.org/W4210892030","https://openalex.org/W4220969874","https://openalex.org/W4225762007","https://openalex.org/W4230056077","https://openalex.org/W4236965008","https://openalex.org/W4241307704","https://openalex.org/W4312355426","https://openalex.org/W4321497572","https://openalex.org/W4366988472","https://openalex.org/W4379620786"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175"],"abstract_inverted_index":{"Motion":[0],"segmentation":[1,46],"and":[2,22,56,60,67,120,154,184,207],"identification":[3],"of":[4,10,69,98,193],"structural":[5],"segments":[6,38],"from":[7],"an":[8,190,205],"ensemble":[9],"human":[11,57,70],"trajectories":[12,101,119,158],"continue":[13],"to":[14,64,112],"be":[15],"a":[16,31],"challenge.":[17],"These":[18],"processes":[19],"entail":[20],"distinguishing":[21],"categorizing":[23],"distinct":[24],"motion":[25,45,93,122,157],"patterns":[26],"manifested":[27],"by":[28,148],"pedestrians":[29],"within":[30,159],"group,":[32],"as":[33,35,167,180],"well":[34],"recognizing":[36],"conspicuous":[37],"that":[39,133],"exhibit":[40],"their":[41],"overall":[42,109],"motion.":[43],"Accurate":[44],"is":[47,86],"vital":[48],"in":[49,88,152],"many":[50],"areas,":[51],"particularly":[52],"computer":[53],"vision,":[54],"automation,":[55],"behavior":[58,215],"investigation":[59],"monitoring.":[61],"However,":[62],"owing":[63],"the":[65,80,99,105,108,115,118,129,134,144,149,181,202],"complexity":[66],"irregularity":[68],"motion,":[71],"this":[72,78,89],"task":[73],"demands":[74],"sophisticated":[75],"procedures.":[76],"Addressing":[77],"challenge,":[79],"Angular":[81],"Gaussian":[82],"Mixture":[83],"Model":[84],"(AGMM)":[85],"proposed":[87,110,135,150],"study":[90,198],"for":[91,204],"visual":[92],"segmentation.":[94,123],"The":[95,124,141],"angular":[96],"features":[97],"pedestrian":[100],"are":[102],"incorporated":[103],"into":[104],"GMM,":[106],"allowing":[107],"framework":[111,136],"accurately":[113,155],"expose":[114],"similarity":[116],"between":[117],"fulfill":[121],"experimental":[125],"findings":[126,142],"conducted":[127],"on":[128],"CUHK":[130],"benchmark":[131],"demonstrate":[132],"outperforms":[137],"various":[138],"state-of-the-art":[139],"methods.":[140],"signify":[143],"superior":[145],"performance":[146],"achieved":[147],"approach":[151],"effectively":[153,200],"segmenting":[156],"different":[160],"crowded":[161],"scenarios.":[162],"Statistical":[163],"evaluation":[164],"approaches,":[165],"such":[166],"normalized":[168],"mutual":[169],"information":[170],"index":[171,175],"(NMI),":[172],"purity,":[173],"rand":[174],"(RI),":[176],"F-measure,":[177],"also":[178],"known":[179],"F1-score":[182],"(F1),":[183],"accuracy":[185],"(ACC),":[186],"were":[187],"utilized":[188],"employing":[189],"immense":[191],"number":[192],"real-world":[194],"video":[195],"clips.":[196],"This":[197],"has":[199],"laid":[201],"foundation":[203],"essential":[206],"initial":[208],"stride":[209],"towards":[210],"achieving":[211],"comprehensive,":[212],"high-level":[213],"crowd":[214],"analysis.":[216]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
