{"id":"https://openalex.org/W2903004773","doi":"https://doi.org/10.1109/icpr.2018.8545414","title":"Multi-scale Semantic Segmentation Enriched Features for Pedestrian Detection","display_name":"Multi-scale Semantic Segmentation Enriched Features for Pedestrian Detection","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903004773","doi":"https://doi.org/10.1109/icpr.2018.8545414","mag":"2903004773"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545414","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545414","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5080834510","display_name":"Xiaolu Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210099079","display_name":"Institute of Intelligent Machines","ror":"https://ror.org/00w0qep84","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I2802624667","https://openalex.org/I4210099079"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolu Xie","raw_affiliation_strings":["Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210099079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103123751","display_name":"Zengfu Wang","orcid":"https://orcid.org/0000-0003-1859-900X"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zengfu Wang","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2196","last_page":"2201"},"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":0.9990000128746033,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9987000226974487,"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":0.8071832656860352},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7834455966949463},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7234535217285156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7088617086410522},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6875588893890381},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6684859991073608},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6166355609893799},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.6021310091018677},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5886646509170532},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4975798428058624},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.4352337718009949},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40933355689048767},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.3613429665565491},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09123453497886658}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8071832656860352},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7834455966949463},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7234535217285156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7088617086410522},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6875588893890381},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6684859991073608},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6166355609893799},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.6021310091018677},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5886646509170532},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4975798428058624},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.4352337718009949},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40933355689048767},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.3613429665565491},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09123453497886658},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545414","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545414","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1836465849","https://openalex.org/W2098064689","https://openalex.org/W2113635748","https://openalex.org/W2117539524","https://openalex.org/W2150066425","https://openalex.org/W2154582212","https://openalex.org/W2159386181","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2490270993","https://openalex.org/W2497039038","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2613599172","https://openalex.org/W2613718673","https://openalex.org/W2950800384","https://openalex.org/W2962850098","https://openalex.org/W2962992847","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6638667902","https://openalex.org/W6674977583","https://openalex.org/W6676855097","https://openalex.org/W6714138976","https://openalex.org/W6723816956","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2922437833","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581","https://openalex.org/W2556125083"],"abstract_inverted_index":{"Pedestrian":[0],"detection,":[1],"as":[2,15,84,163],"a":[3,27,43,74],"branch":[4],"of":[5,93],"computer":[6],"vision,":[7],"has":[8],"many":[9],"significant":[10],"real":[11],"world":[12],"applications":[13],"such":[14],"autonomous":[16],"driving":[17],"or":[18],"human":[19],"behavior":[20],"analysis.":[21],"In":[22],"this":[23],"paper,":[24],"we":[25],"propose":[26],"convolutional":[28,122],"neural":[29,123],"network":[30,66,144],"(CNN)":[31],"based":[32,125],"pedestrian":[33],"detection":[34,54,126,146,160],"framework":[35],"which":[36],"can":[37],"be":[38,103],"trained":[39],"end-to-end.":[40],"We":[41,135],"design":[42],"feature":[44,57,61,91,100,113,133,156],"enrichment":[45,58,114,157],"unit":[46,115],"to":[47,52,108,118,141],"produce":[48,79,109],"more":[49],"representative":[50],"features":[51,72,86],"improve":[53,159],"performance.":[55],"The":[56,112,154],"units":[59,158],"receive":[60],"maps":[62,92,101],"from":[63],"the":[64,94,98,106,143,166],"body":[65,95],"layer":[67,69],"by":[68,165],"and":[70,87,131,147,150],"convey":[71],"in":[73],"backward":[75],"manner.":[76],"Together":[77],"they":[78],"multi-scale":[80,155],"semantic":[81],"segmentation":[82,148],"results":[83],"extra":[85],"merge":[88],"them":[89],"with":[90],"network.":[96],"Then":[97],"merged":[99],"will":[102],"fed":[104],"into":[105,120],"detector":[107],"final":[110],"predictions.":[111],"is":[116],"easy":[117],"embed":[119],"existing":[121],"networks":[124],"frameworks":[127],"since":[128],"it":[129],"receives":[130],"produces":[132],"maps.":[134],"use":[136],"an":[137],"alternating":[138],"training":[139],"strategy":[140],"train":[142],"for":[145],"respectively":[149],"achieve":[151],"considerable":[152],"accuracy.":[153],"accuracy":[161],"significantly":[162],"proven":[164],"experiments.":[167]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
