{"id":"https://openalex.org/W3092665420","doi":"https://doi.org/10.1145/3394171.3413989","title":"Box Guided Convolution for Pedestrian Detection","display_name":"Box Guided Convolution for Pedestrian Detection","publication_year":2020,"publication_date":"2020-10-12","ids":{"openalex":"https://openalex.org/W3092665420","doi":"https://doi.org/10.1145/3394171.3413989","mag":"3092665420"},"language":"en","primary_location":{"id":"doi:10.1145/3394171.3413989","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","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/A5100782322","display_name":"Jinpeng Li","orcid":"https://orcid.org/0000-0002-1538-7274"},"institutions":[{"id":"https://openalex.org/I4210116052","display_name":"Inception Institute of Artificial Intelligence","ror":"https://ror.org/02664zk40","country_code":"AE","type":"facility","lineage":["https://openalex.org/I4210116052"]}],"countries":["AE"],"is_corresponding":true,"raw_author_name":"Jinpeng Li","raw_affiliation_strings":["Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE"],"affiliations":[{"raw_affiliation_string":"Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE","institution_ids":["https://openalex.org/I4210116052"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019092879","display_name":"Shengcai Liao","orcid":"https://orcid.org/0000-0001-8941-2295"},"institutions":[{"id":"https://openalex.org/I4210116052","display_name":"Inception Institute of Artificial Intelligence","ror":"https://ror.org/02664zk40","country_code":"AE","type":"facility","lineage":["https://openalex.org/I4210116052"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Shengcai Liao","raw_affiliation_strings":["Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE"],"affiliations":[{"raw_affiliation_string":"Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE","institution_ids":["https://openalex.org/I4210116052"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079797711","display_name":"Hangzhi Jiang","orcid":"https://orcid.org/0000-0002-7496-6065"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hangzhi Jiang","raw_affiliation_strings":["University of Chinese Academy of Sciences & Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences & Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082634513","display_name":"Ling Shao","orcid":"https://orcid.org/0000-0002-8264-6117"},"institutions":[{"id":"https://openalex.org/I4210116052","display_name":"Inception Institute of Artificial Intelligence","ror":"https://ror.org/02664zk40","country_code":"AE","type":"facility","lineage":["https://openalex.org/I4210116052"]},{"id":"https://openalex.org/I4210113480","display_name":"Mohamed bin Zayed University of Artificial Intelligence","ror":"https://ror.org/0258gkt32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210113480"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Ling Shao","raw_affiliation_strings":["Inception Institute of Artificial Intelligence (IIAI) & Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE"],"affiliations":[{"raw_affiliation_string":"Inception Institute of Artificial Intelligence (IIAI) & Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE","institution_ids":["https://openalex.org/I4210113480","https://openalex.org/I4210116052"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100782322"],"corresponding_institution_ids":["https://openalex.org/I4210116052"],"apc_list":null,"apc_paid":null,"fwci":1.7586,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.8731263,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1615","last_page":"1624"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9990000128746033,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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":0.7471762895584106},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7349152565002441},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6975923776626587},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.6515599489212036},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6032009124755859},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5355517864227295},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5099588632583618},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4872223436832428},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4455832540988922},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.4428228437900543},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.41304266452789307},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39822518825531006},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37140533328056335},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1391540765762329},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10200199484825134},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08608996868133545},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07378724217414856}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7471762895584106},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7349152565002441},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6975923776626587},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.6515599489212036},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6032009124755859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5355517864227295},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5099588632583618},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4872223436832428},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4455832540988922},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.4428228437900543},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.41304266452789307},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39822518825531006},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37140533328056335},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1391540765762329},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10200199484825134},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08608996868133545},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07378724217414856},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394171.3413989","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1475617732","https://openalex.org/W1536680647","https://openalex.org/W1677182931","https://openalex.org/W1923697677","https://openalex.org/W1966500826","https://openalex.org/W1976818984","https://openalex.org/W1984795473","https://openalex.org/W2088049833","https://openalex.org/W2094056275","https://openalex.org/W2108598243","https://openalex.org/W2125556102","https://openalex.org/W2140090057","https://openalex.org/W2156547346","https://openalex.org/W2194775991","https://openalex.org/W2200528286","https://openalex.org/W2340897893","https://openalex.org/W2578456052","https://openalex.org/W2594507094","https://openalex.org/W2597507805","https://openalex.org/W2601564443","https://openalex.org/W2766070280","https://openalex.org/W2780664485","https://openalex.org/W2798542761","https://openalex.org/W2894299524","https://openalex.org/W2899771611","https://openalex.org/W2954232934","https://openalex.org/W2962850098","https://openalex.org/W2963037989","https://openalex.org/W2963351448","https://openalex.org/W2963549237","https://openalex.org/W2963681621","https://openalex.org/W2963769056","https://openalex.org/W2963786238","https://openalex.org/W2963927307","https://openalex.org/W2964241181","https://openalex.org/W2982770724","https://openalex.org/W2985405845"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2608226141","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W4313315626","https://openalex.org/W4388653314","https://openalex.org/W4385725200","https://openalex.org/W2101531944","https://openalex.org/W2892760082","https://openalex.org/W4327500857"],"abstract_inverted_index":{"Occlusions,":[0],"scale":[1],"variation":[2],"and":[3,20,35,134,146,169,195,208],"numerous":[4],"false":[5,167],"positives":[6,168,172],"still":[7],"represent":[8],"fundamental":[9],"challenges":[10,41,68],"in":[11,70],"pedestrian":[12,49,192],"detection.":[13,156],"Intuitively,":[14],"different":[15],"sizes":[16,88],"of":[17,89,110,113,120,126,139],"receptive":[18,104],"fields":[19,105],"more":[21],"attention":[22],"to":[23,65,106,148,154,165],"the":[24,87,94,108,118,123,132,144,197,201],"visible":[25,136],"parts":[26],"are":[27,128],"required":[28],"for":[29,117],"detecting":[30],"pedestrians":[31],"with":[32,200],"various":[33],"scales":[34],"occlusion":[36],"levels,":[37],"respectively.":[38],"However,":[39],"these":[40,67],"have":[42],"not":[43],"been":[44],"addressed":[45],"well":[46],"by":[47,93,173],"existing":[48],"detectors.":[50],"This":[51],"paper":[52],"presents":[53],"a":[54,71,78,140,158],"novel":[55],"convolutional":[56],"network,":[57],"denoted":[58],"as":[59,143,179],"box":[60,79],"guided":[61,80,92],"convolution":[62,81,90],"network":[63],"(BGCNet),":[64],"tackle":[66],"simultaneously":[69],"unified":[72],"framework.":[73],"In":[74,98,115],"particular,":[75],"we":[76],"proposed":[77],"(BGC)":[82],"that":[83],"can":[84],"dynamically":[85],"adjust":[86],"kernels":[91],"predicted":[95],"bounding":[96],"boxes.":[97],"this":[99],"way,":[100],"BGCNet":[101,189],"provides":[102],"position-aware":[103],"address":[107],"challenge":[109],"large":[111],"variations":[112],"scales.":[114],"addition,":[116],"issue":[119],"heavy":[121],"occlusion,":[122],"kernel":[124],"parameters":[125],"BGC":[127],"spatially":[129],"localized":[130],"around":[131],"salient":[133],"mostly":[135],"key":[137],"points":[138],"pedestrian,":[141],"such":[142],"head":[145],"foot,":[147],"effectively":[149],"capture":[150],"high-level":[151],"semantic":[152],"features":[153],"help":[155],"Furthermore,":[157],"local":[159,180],"maximum":[160],"(LM)":[161],"loss":[162],"is":[163],"introduced":[164],"depress":[166],"highlight":[170],"true":[171],"forcing":[174],"positives,":[175],"rather":[176],"than":[177],"negatives,":[178],"maximums,":[181],"without":[182],"any":[183],"additional":[184],"inference":[185],"burden.":[186],"We":[187],"evaluate":[188],"on":[190,205],"popular":[191],"detection":[193],"benchmarks,":[194],"achieve":[196],"state-of-the-art":[198],"results,":[199],"significant":[202],"performance":[203],"improvement":[204],"heavily":[206],"occluded":[207],"small-scale":[209],"pedestrians.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}