{"id":"https://openalex.org/W2587968883","doi":"https://doi.org/10.1109/ssci.2016.7850112","title":"Pedestrian detection aided by scale-discriminative network","display_name":"Pedestrian detection aided by scale-discriminative network","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2587968883","doi":"https://doi.org/10.1109/ssci.2016.7850112","mag":"2587968883"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2016.7850112","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2016.7850112","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5089642905","display_name":"Zongqing Lu","orcid":"https://orcid.org/0000-0003-3967-2704"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zongqing Lu","raw_affiliation_strings":["Department of Electronic Engineering, Shenzhen Key Lab. of Information Sci & Tech/Shenzhen Engineering Lab. of IS & DRM, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Shenzhen Key Lab. of Information Sci & Tech/Shenzhen Engineering Lab. of IS & DRM, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101609299","display_name":"Wenjian Zhang","orcid":"https://orcid.org/0009-0003-2696-2398"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenjian Zhang","raw_affiliation_strings":["Department of Electronic Engineering, Shenzhen Key Lab. of Information Sci & Tech/Shenzhen Engineering Lab. of IS & DRM, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Shenzhen Key Lab. of Information Sci & Tech/Shenzhen Engineering Lab. of IS & DRM, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009239895","display_name":"Qingmin Liao","orcid":"https://orcid.org/0000-0002-7509-3964"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qingmin Liao","raw_affiliation_strings":["Department of Electronic Engineering, Shenzhen Key Lab. of Information Sci & Tech/Shenzhen Engineering Lab. of IS & DRM, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Shenzhen Key Lab. of Information Sci & Tech/Shenzhen Engineering Lab. of IS & DRM, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5089642905"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.167,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60868981,"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":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.998199999332428,"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":0.998199999332428,"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":0.9972000122070312,"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":0.9965999722480774,"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.9268368482589722},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.885096549987793},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7780622839927673},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7502346038818359},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6873599290847778},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6252121925354004},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6006889343261719},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5456292629241943},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.43273961544036865},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4057510793209076},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1134946346282959}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.9268368482589722},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.885096549987793},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7780622839927673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7502346038818359},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6873599290847778},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6252121925354004},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6006889343261719},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5456292629241943},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.43273961544036865},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4057510793209076},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1134946346282959},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/ssci.2016.7850112","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2016.7850112","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1475617732","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1882819926","https://openalex.org/W1903029394","https://openalex.org/W1903127635","https://openalex.org/W2031454541","https://openalex.org/W2037227137","https://openalex.org/W2074777933","https://openalex.org/W2077513643","https://openalex.org/W2081021369","https://openalex.org/W2102605133","https://openalex.org/W2107775979","https://openalex.org/W2125556102","https://openalex.org/W2136724559","https://openalex.org/W2155893237","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2179352600","https://openalex.org/W2200528286","https://openalex.org/W2586823423","https://openalex.org/W2962992847","https://openalex.org/W2963315052","https://openalex.org/W3097096317","https://openalex.org/W6637373629","https://openalex.org/W6639461879","https://openalex.org/W6676338569","https://openalex.org/W6684191040","https://openalex.org/W6733012444"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W1487808658","https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2922437833","https://openalex.org/W4223892596","https://openalex.org/W4312696271","https://openalex.org/W2933098581"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"is":[2,16],"greatly":[3],"successful":[4],"when":[5],"used":[6],"for":[7,19,62,88],"pedestrian":[8,64,89,100],"detection.":[9,21],"However,":[10],"we":[11,42,66],"find":[12],"that":[13,49,71,107],"this":[14,38,95],"method":[15],"barely":[17],"satisfactory":[18],"multi-scale":[20,27],"Meanwhile,":[22],"various":[23],"solutions":[24],"such":[25],"as":[26],"classifiers":[28,52],"have":[29],"been":[30],"developed":[31],"(based":[32],"on":[33],"traditional":[34],"methods)":[35],"to":[36,53,97],"handle":[37],"situation.":[39],"Considering":[40],"this,":[41],"propose":[43],"a":[44,68,84],"scale-discriminative":[45,85],"classifier":[46],"layer":[47,70],"(SDC)":[48],"contains":[50],"numerous":[51],"cope":[54],"with":[55],"different":[56],"scales.":[57],"To":[58],"expand":[59],"the":[60,81,98,103,108],"capacity":[61],"small-scale":[63],"detection,":[65],"construct":[67],"full-scale":[69],"converges":[72],"both":[73],"high-level":[74],"semantic":[75],"features":[76],"and":[77,102],"low-level":[78],"features.":[79],"From":[80],"analysis":[82],"above,":[83],"network":[86,96],"(SDN)":[87],"detection":[90],"was":[91],"born.":[92],"We":[93],"apply":[94],"Caltech":[99],"dataset,":[101],"experimental":[104],"results":[105],"show":[106],"SDN":[109],"achieves":[110],"state-of-the-art":[111],"performance.":[112]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
