{"id":"https://openalex.org/W2789783969","doi":"https://doi.org/10.1109/vcip.2017.8305054","title":"Mask-streaming CNN for pedestrian detection","display_name":"Mask-streaming CNN for pedestrian detection","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2789783969","doi":"https://doi.org/10.1109/vcip.2017.8305054","mag":"2789783969"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2017.8305054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","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/A5011842388","display_name":"Peilei Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peilei Dong","raw_affiliation_strings":["School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017052768","display_name":"Wenmin Wang","orcid":"https://orcid.org/0000-0003-2664-4413"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenmin Wang","raw_affiliation_strings":["School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032782101","display_name":"Mengdi Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengdi Fan","raw_affiliation_strings":["School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060843253","display_name":"Ronggang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ronggang Wang","raw_affiliation_strings":["School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100447691","display_name":"Ge Li","orcid":"https://orcid.org/0000-0003-0140-0949"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Li","raw_affiliation_strings":["School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Computer Engineering, Peking University, Shenzhen, Guangdong Province, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5011842388"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.091,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.50850475,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9995999932289124,"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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9958999752998352,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8500063419342041},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7352146506309509},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7021650075912476},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6567670106887817},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6493721604347229},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6463793516159058},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5194261074066162},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5114449262619019},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4209126830101013}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8500063419342041},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7352146506309509},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7021650075912476},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6567670106887817},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6493721604347229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6463793516159058},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5194261074066162},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5114449262619019},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4209126830101013},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip.2017.8305054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7799999713897705}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1986905809","https://openalex.org/W2010181071","https://openalex.org/W2088049833","https://openalex.org/W2125556102","https://openalex.org/W2155893237","https://openalex.org/W2156547346","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2162741153","https://openalex.org/W2168356304","https://openalex.org/W2200528286","https://openalex.org/W2548197316","https://openalex.org/W6600313631","https://openalex.org/W6646904105","https://openalex.org/W6653248861"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W2392100589","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2751005898"],"abstract_inverted_index":{"Inspired":[0],"by":[1,59],"humans":[2],"recognition":[3],"of":[4,21,41,55,72,87],"pedestrians,":[5],"we":[6],"propose":[7],"a":[8,53,77],"mask-streaming":[9],"convolutional":[10],"neural":[11],"network":[12,68],"(CNN)":[13],"for":[14],"pedestrian":[15,115],"detection.":[16],"The":[17],"mask":[18,64],"stream":[19,65],"consists":[20],"one":[22],"original":[23,57],"region":[24,58],"proposal":[25],"and":[26,45,98,108],"six":[27],"masked":[28,31,74],"regions.":[29],"These":[30],"regions,":[32],"which":[33],"aim":[34],"at":[35],"highlighting":[36],"significantly":[37],"discriminative":[38],"semantic":[39],"characteristic":[40],"head,":[42],"body":[43],"parts":[44],"contextual":[46],"information,":[47],"are":[48],"generated":[49],"through":[50],"wiping":[51],"off":[52],"part":[54],"the":[56,63,67,84,91,95,106],"`masks'.":[60],"We":[61,89],"feed":[62],"into":[66],"to":[69,113],"generate":[70],"features":[71,82],"each":[73],"region,":[75],"then":[76],"concatenate":[78],"layer":[79],"integrates":[80],"these":[81],"as":[83],"final":[85],"representation":[86],"pedestrians.":[88],"evaluate":[90],"proposed":[92],"model":[93],"on":[94],"challenging":[96],"Caltech":[97],"Eth":[99],"datasets.":[100],"Our":[101],"approach":[102],"performs":[103],"better":[104],"than":[105],"baseline,":[107],"achieves":[109],"competitive":[110],"performance":[111],"compared":[112],"numerous":[114],"detection":[116],"methods.":[117]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}