{"id":"https://openalex.org/W2514654788","doi":"https://doi.org/10.1109/icip.2016.7532551","title":"End-to-end crowd counting via joint learning local and global count","display_name":"End-to-end crowd counting via joint learning local and global count","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2514654788","doi":"https://doi.org/10.1109/icip.2016.7532551","mag":"2514654788"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532551","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532551","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/A5090459802","display_name":"Chong Shang","orcid":"https://orcid.org/0000-0003-4822-1489"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chong Shang","raw_affiliation_strings":["Depart. of Computer Sci. & Tech., Tsinghua University, Beijing, China","Tsinghua National Lab for Info. Sci. & Tech., Depart. of Computer Sci. & Tech., Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Depart. of Computer Sci. & Tech., Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua National Lab for Info. Sci. & Tech., Depart. of Computer Sci. & Tech., Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074577884","display_name":"Haizhou Ai","orcid":"https://orcid.org/0000-0002-0166-5755"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haizhou Ai","raw_affiliation_strings":["Depart. of Computer Sci. & Tech., Tsinghua University, Beijing, China","Tsinghua National Lab for Info. Sci. & Tech., Depart. of Computer Sci. & Tech., Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Depart. of Computer Sci. & Tech., Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua National Lab for Info. Sci. & Tech., Depart. of Computer Sci. & Tech., Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001405009","display_name":"Bo Bai","orcid":"https://orcid.org/0000-0003-4796-8249"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Bai","raw_affiliation_strings":["Huawei Technologies, Beijing, China","Huawei Technologies, Beijing, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Huawei Technologies, Beijing, China","institution_ids":["https://openalex.org/I2250955327"]},{"raw_affiliation_string":"Huawei Technologies, Beijing, China#TAB#","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5090459802"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":13.8612,"has_fulltext":false,"cited_by_count":206,"citation_normalized_percentile":{"value":0.9922465,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1215","last_page":"1219"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9973999857902527,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8132855892181396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6953045129776001},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6736316680908203},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5434868335723877},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5254275798797607},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.49259334802627563},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48219338059425354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47986549139022827},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.44960999488830566},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4495748281478882},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4313455820083618},{"id":"https://openalex.org/keywords/counting-problem","display_name":"Counting problem","score":0.4299781322479248},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.12634578347206116}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8132855892181396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6953045129776001},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6736316680908203},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5434868335723877},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5254275798797607},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.49259334802627563},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48219338059425354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47986549139022827},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.44960999488830566},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4495748281478882},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4313455820083618},{"id":"https://openalex.org/C16592021","wikidata":"https://www.wikidata.org/wiki/Q5177154","display_name":"Counting problem","level":2,"score":0.4299781322479248},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.12634578347206116},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532551","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532551","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W295732247","https://openalex.org/W607748843","https://openalex.org/W1542079534","https://openalex.org/W1686810756","https://openalex.org/W1910776219","https://openalex.org/W1977069908","https://openalex.org/W1978232622","https://openalex.org/W2017910661","https://openalex.org/W2072232009","https://openalex.org/W2075875861","https://openalex.org/W2097117768","https://openalex.org/W2121864252","https://openalex.org/W2123175289","https://openalex.org/W2145983039","https://openalex.org/W2160372426","https://openalex.org/W2161841955","https://openalex.org/W2164990725","https://openalex.org/W2165609887","https://openalex.org/W2207893099","https://openalex.org/W2962835968","https://openalex.org/W6618526183","https://openalex.org/W6632569617","https://openalex.org/W6639759915","https://openalex.org/W6674914833","https://openalex.org/W6681368121"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2046435967","https://openalex.org/W4231775656","https://openalex.org/W4313906399","https://openalex.org/W4321487865","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2318112981","https://openalex.org/W4312417841"],"abstract_inverted_index":{"Crowd":[0],"counting":[1,21,66,117,134],"is":[2],"a":[3,56,99,104],"very":[4],"challenging":[5,132],"task":[6],"in":[7],"crowded":[8],"scenes":[9],"due":[10],"to":[11,37,98,102,115],"heavy":[12],"occlusions,":[13],"appearance":[14],"variations":[15],"and":[16,62,88,141],"perspective":[17],"distortions.":[18],"Current":[19],"crowd":[20,133],"methods":[22],"typically":[23],"operate":[24],"on":[25,130],"an":[26,47],"image":[27,58,97],"patch":[28],"level":[29,108],"with":[30,123],"overlaps,":[31],"then":[32],"sum":[33],"over":[34,74],"the":[35,39,65,96,111,128,138,143],"patches":[36],"get":[38,103],"final":[40],"count.":[41,90],"In":[42,91],"this":[43],"paper,":[44],"we":[45,93],"propose":[46],"end-to-end":[48],"convolutional":[49],"neural":[50],"network":[51,121],"(CNN)":[52],"architecture":[53],"that":[54],"takes":[55,79],"whole":[57],"as":[59],"its":[60],"input":[61],"directly":[63],"outputs":[64],"result.":[67],"While":[68],"making":[69],"use":[70],"of":[71,81,106,145],"sharing":[72],"computations":[73],"overlapping":[75],"regions,":[76],"our":[77,146],"method":[78],"advantages":[80],"contextual":[82],"information":[83],"when":[84],"predicting":[85],"both":[86],"local":[87,116],"global":[89],"particular,":[92],"first":[94],"feed":[95],"pre-trained":[100],"CNN":[101],"set":[105],"high":[107],"features.":[109],"Then":[110],"features":[112],"are":[113],"mapped":[114],"numbers":[118],"using":[119],"recurrent":[120],"layers":[122],"memory":[124],"cells.":[125],"We":[126],"perform":[127],"experiments":[129],"several":[131],"datasets,":[135],"which":[136],"achieve":[137],"state-of-the-art":[139],"results":[140],"demonstrate":[142],"effectiveness":[144],"method.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":29},{"year":2020,"cited_by_count":40},{"year":2019,"cited_by_count":38},{"year":2018,"cited_by_count":34},{"year":2017,"cited_by_count":11}],"updated_date":"2026-02-22T13:39:03.778224","created_date":"2025-10-10T00:00:00"}
