{"id":"https://openalex.org/W2394843433","doi":"https://doi.org/10.1109/wacv.2016.7477682","title":"Crowd density estimation based on rich features and random projection forest","display_name":"Crowd density estimation based on rich features and random projection forest","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2394843433","doi":"https://doi.org/10.1109/wacv.2016.7477682","mag":"2394843433"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2016.7477682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2016.7477682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101963090","display_name":"Bolei Xu","orcid":"https://orcid.org/0000-0002-8589-7261"},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bolei Xu","raw_affiliation_strings":["The University of Nottingham, Ningbo, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Nottingham, Ningbo, China","institution_ids":["https://openalex.org/I13591777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114259459","display_name":"Guoping Qiu","orcid":"https://orcid.org/0000-0002-5877-5648"},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoping Qiu","raw_affiliation_strings":["The University of Nottingham, Ningbo, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Nottingham, Ningbo, China","institution_ids":["https://openalex.org/I13591777"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I13591777"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":92,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9987999796867371,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.977400004863739,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7481385469436646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7454591989517212},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.6413811445236206},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5668211579322815},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5602888464927673},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5393763184547424},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.52234947681427},{"id":"https://openalex.org/keywords/random-projection","display_name":"Random projection","score":0.5101252198219299},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5052624344825745},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.5027613639831543},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.49161702394485474},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46512529253959656},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42850661277770996},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4255560636520386},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35547614097595215},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.31879180669784546},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23946154117584229},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15470531582832336},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13439950346946716},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.13351210951805115},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10596063733100891}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7481385469436646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7454591989517212},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.6413811445236206},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5668211579322815},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5602888464927673},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5393763184547424},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.52234947681427},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.5101252198219299},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5052624344825745},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.5027613639831543},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.49161702394485474},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46512529253959656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42850661277770996},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4255560636520386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35547614097595215},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.31879180669784546},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23946154117584229},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15470531582832336},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13439950346946716},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.13351210951805115},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10596063733100891},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv.2016.7477682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2016.7477682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.5099999904632568,"display_name":"Life in Land"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320324","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58"},{"id":"https://openalex.org/F4320337088","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1573412753","https://openalex.org/W1590183771","https://openalex.org/W1910776219","https://openalex.org/W1973650920","https://openalex.org/W1974744233","https://openalex.org/W1976959044","https://openalex.org/W1977069908","https://openalex.org/W1978373643","https://openalex.org/W1990653740","https://openalex.org/W1992825118","https://openalex.org/W2053171205","https://openalex.org/W2075875861","https://openalex.org/W2088658556","https://openalex.org/W2089497633","https://openalex.org/W2096229530","https://openalex.org/W2097324787","https://openalex.org/W2098554822","https://openalex.org/W2101178587","https://openalex.org/W2114588272","https://openalex.org/W2123175289","https://openalex.org/W2130822989","https://openalex.org/W2143043044","https://openalex.org/W2144796713","https://openalex.org/W2150123015","https://openalex.org/W2151666244","https://openalex.org/W2151928481","https://openalex.org/W2158979073","https://openalex.org/W2160109814","https://openalex.org/W2161841955","https://openalex.org/W2161969291","https://openalex.org/W2165368973","https://openalex.org/W2170172268","https://openalex.org/W2548197316","https://openalex.org/W2798909945","https://openalex.org/W2911964244","https://openalex.org/W2979473749","https://openalex.org/W3151111735","https://openalex.org/W6683411478"],"related_works":["https://openalex.org/W3034924094","https://openalex.org/W3094954546","https://openalex.org/W1488708774","https://openalex.org/W1982811510","https://openalex.org/W4391100477","https://openalex.org/W2402189625","https://openalex.org/W4283771152","https://openalex.org/W2142518521","https://openalex.org/W2232109354","https://openalex.org/W2141742840"],"abstract_inverted_index":{"Current":[0],"state":[1,167],"of":[2,28,44,67,127,168,177],"the":[3,65,96,119,125,134,169],"art":[4,170],"crowd":[5,68,82],"density":[6,83],"estimation":[7],"methods":[8],"are":[9,173],"based":[10],"on":[11,154],"computationally":[12],"expensive":[13],"Gaussian":[14],"process":[15],"regression":[16,19,97,180],"or":[17],"Ridge":[18],"models":[20],"which":[21],"can":[22,47],"only":[23],"handle":[24],"a":[25,41],"small":[26],"number":[27],"features.":[29],"In":[30,52],"many":[31],"computer":[32],"vision":[33],"applications,":[34],"it":[35],"has":[36],"been":[37],"empirically":[38],"shown":[39],"that":[40,57,162,172],"richer":[42,78],"set":[43],"image":[45,60],"features":[46,61],"lead":[48],"to":[49,73,110,122,130,175],"enhanced":[50],"performances.":[51],"this":[53,139],"paper,":[54],"we":[55,91,114],"reason":[56],"using":[58],"more":[59],"could":[62],"potentially":[63],"boost":[64],"performances":[66,171],"detection":[69,158],"and":[70,77,89,105,129,150],"thus":[71,143],"propose":[72],"employ":[74],"much":[75],"extensive":[76],"feature":[79],"sets":[80],"for":[81],"estimation.":[84],"To":[85],"achieve":[86],"computational":[87],"efficiency":[88],"scalability,":[90],"use":[92],"random":[93,111,116],"forest":[94,112],"as":[95],"model":[98],"whose":[99],"tree":[100,120,135],"structure":[101],"is":[102],"intrinsically":[103],"fast":[104],"scalable.":[106],"Unlike":[107],"traditional":[108],"approaches":[109],"construction,":[113],"embed":[115],"projection":[117],"in":[118,133],"nodes":[121],"simultaneously":[123],"combat":[124],"curse":[126],"dimensionality":[128],"introduce":[131],"randomness":[132],"construction":[136],"(we":[137],"call":[138],"Random":[140],"Projection":[141],"Forest)":[142],"making":[144],"our":[145,163],"new":[146,164],"method":[147,165],"very":[148],"efficient":[149],"effective.":[151],"Experimental":[152],"results":[153],"two":[155],"public":[156],"pedestrian":[157],"video":[159],"databases":[160],"show":[161],"achieves":[166],"superior":[174],"those":[176],"previously":[178],"published":[179],"techniques.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":22},{"year":2019,"cited_by_count":23},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}