{"id":"https://openalex.org/W2897932648","doi":"https://doi.org/10.1109/ijcnn.2018.8489310","title":"Multi ROI and Multi Map Networks for Accurate and Efficient Pedestrian Detection","display_name":"Multi ROI and Multi Map Networks for Accurate and Efficient Pedestrian Detection","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2897932648","doi":"https://doi.org/10.1109/ijcnn.2018.8489310","mag":"2897932648"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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/A5062672609","display_name":"Zhe Qiu","orcid":"https://orcid.org/0009-0003-3174-8679"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Qiu","raw_affiliation_strings":["Department of Electronic Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101893294","display_name":"Xiaodong Gu","orcid":"https://orcid.org/0000-0003-2515-8050"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Gu","raw_affiliation_strings":["Department of Electronic Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9993000030517578,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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":0.8360545039176941},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7245703935623169},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.678188145160675},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.665023148059845},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5733583569526672},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5702441930770874},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5302422046661377},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5272516012191772},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5039045214653015},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4894231855869293},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.4810755252838135},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47955700755119324},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.4511718153953552},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40410366654396057},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.3180996775627136},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18079537153244019}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8360545039176941},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7245703935623169},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.678188145160675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.665023148059845},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5733583569526672},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5702441930770874},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5302422046661377},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5272516012191772},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5039045214653015},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4894231855869293},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4810755252838135},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47955700755119324},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.4511718153953552},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40410366654396057},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.3180996775627136},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18079537153244019},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7099999785423279}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309398","display_name":"California Institute of Technology","ror":"https://ror.org/05dxps055"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1903127635","https://openalex.org/W1958328135","https://openalex.org/W1976818984","https://openalex.org/W2074777933","https://openalex.org/W2104446196","https://openalex.org/W2125556102","https://openalex.org/W2155893237","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2170101770","https://openalex.org/W2497039038","https://openalex.org/W2613718673","https://openalex.org/W2791697444","https://openalex.org/W2963263410","https://openalex.org/W2963516811","https://openalex.org/W6620707391","https://openalex.org/W6675396308","https://openalex.org/W6684811926","https://openalex.org/W6723816956"],"related_works":["https://openalex.org/W4237171675","https://openalex.org/W3036286480","https://openalex.org/W4287027631","https://openalex.org/W3192357901","https://openalex.org/W2387360586","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W4390524233"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,81,208],"as":[2,14,45],"a":[3,93,97,214],"key":[4],"problem":[5],"of":[6,29,160],"computer":[7],"vision":[8],"has":[9,40],"many":[10],"comprehensive":[11],"applications":[12],"such":[13],"smart":[15],"transportation,":[16],"video":[17],"surveillance":[18],"and":[19,58,70,77,96,151,168,188,202,205,226],"self-driving":[20],"car.":[21],"In":[22,171],"this":[23],"work,":[24],"we":[25,46,64,91,181,198],"explore":[26],"the":[27,68,71,80,111,123,155,158,200,223],"application":[28],"Faster":[30,38,52,179],"R-CNN":[31,39,53],"for":[32,51],"pedestrian":[33],"detection.":[34],"We":[35],"found":[36,65],"that":[37,66],"not":[41,108],"shown":[42],"competitive":[43,215],"results":[44,82],"expected.":[47],"The":[48,105,135],"main":[49],"challenges":[50],"may":[54],"be":[55,84],"small":[56],"objects":[57],"hard":[59],"negative":[60],"examples.":[61],"More":[62],"interestingly,":[63],"when":[67],"RPN":[69,201],"proposal":[72],"classifier":[73],"are":[74],"trained":[75,199],"separately":[76,204],"simply":[78],"combined,":[79],"can":[83],"significantly":[85],"improved.":[86],"Based":[87],"on":[88,222],"these":[89,102,161,175,232],"observations,":[90],"designed":[92],"multi-map":[94,106],"network":[95,99,107,137,156,192],"multi-ROI":[98,136],"to":[100,142,173,190],"solve":[101],"two":[103,176],"problems.":[104],"only":[109],"utilizes":[110],"convolutional":[112,118],"feature":[113,125],"maps":[114,126],"output":[115],"by":[116],"last":[117],"layer":[119],"but":[120,131],"also":[121,182],"exploits":[122],"previous":[124],"which":[127],"have":[128],"higher":[129],"resolution":[130],"less":[132],"semantic":[133],"information.":[134],"converts":[138],"one":[139,150,166,186],"bounding":[140,144,163],"box":[141],"three":[143,162],"boxes":[145,164],"including":[146],"enlarged":[147],"one,":[148],"shrinking":[149],"original":[152],"one.":[153],"Then,":[154],"fuses":[157],"features":[159],"into":[165,185],"robust":[167],"discriminative":[169],"feature.":[170],"addition":[172],"comparing":[174],"networks":[177],"with":[178,218],"R-CNN,":[180],"merged":[183],"them":[184],"unified":[187],"end":[189,191],"called":[193],"multi-ROI-map":[194],"(MRM)":[195],"network.":[196],"Finally,":[197],"MRM":[203],"fused":[206],"their":[207],"results.":[209],"Overall,":[210],"our":[211],"method":[212],"achieves":[213],"result":[216],"compared":[217],"some":[219],"leading":[220],"methods":[221],"Caltech":[224],"dataset":[225],"executes":[227],"2":[228],"times":[229],"faster":[230],"than":[231],"methods.":[233]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}