{"id":"https://openalex.org/W2075173352","doi":"https://doi.org/10.1109/icce-tw.2014.6904064","title":"Pedestrian detection using hybrid features","display_name":"Pedestrian detection using hybrid features","publication_year":2014,"publication_date":"2014-05-01","ids":{"openalex":"https://openalex.org/W2075173352","doi":"https://doi.org/10.1109/icce-tw.2014.6904064","mag":"2075173352"},"language":"en","primary_location":{"id":"doi:10.1109/icce-tw.2014.6904064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw.2014.6904064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Consumer Electronics - Taiwan","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/A5028504321","display_name":"Hsu-Yung Cheng","orcid":"https://orcid.org/0000-0002-8342-7450"},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Hsu-Yung Cheng","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Central University, Taiwan","Dept. of Computer Science & Information Engineering, National Central University, Taiwan#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Central University, Taiwan","institution_ids":["https://openalex.org/I22265921"]},{"raw_affiliation_string":"Dept. of Computer Science & Information Engineering, National Central University, Taiwan#TAB#","institution_ids":["https://openalex.org/I22265921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043757361","display_name":"You-Jhen Zeng","orcid":null},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"You-Jhen Zeng","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Central University, Taiwan","Dept. of Computer Science & Information Engineering, National Central University, Taiwan#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Central University, Taiwan","institution_ids":["https://openalex.org/I22265921"]},{"raw_affiliation_string":"Dept. of Computer Science & Information Engineering, National Central University, Taiwan#TAB#","institution_ids":["https://openalex.org/I22265921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062678423","display_name":"Chia-Fang Chai","orcid":null},"institutions":[{"id":"https://openalex.org/I189290911","display_name":"National Chung Shan Institute of Science and Technology","ror":"https://ror.org/034ezwg26","country_code":"TW","type":"government","lineage":["https://openalex.org/I189290911"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chia-Fang Chai","raw_affiliation_strings":["Armaments Bureau, Chung-Shan Institute of Science and Technology, Taiwan","Information Management Center, Chung-Shan Institute of Science and Technology, Armaments Bureau, Taiwan"],"affiliations":[{"raw_affiliation_string":"Armaments Bureau, Chung-Shan Institute of Science and Technology, Taiwan","institution_ids":["https://openalex.org/I189290911"]},{"raw_affiliation_string":"Information Management Center, Chung-Shan Institute of Science and Technology, Armaments Bureau, Taiwan","institution_ids":["https://openalex.org/I189290911"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028504321"],"corresponding_institution_ids":["https://openalex.org/I22265921"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13308367,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"213","last_page":"214"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9934999942779541,"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":0.8603925704956055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.773081362247467},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7712571620941162},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6654899716377258},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5970761775970459},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5959826707839966},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5721656084060669},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.5698987245559692},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5640138387680054},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5493495464324951},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5306474566459656},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5268104076385498},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5076388120651245},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5034844279289246},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.44660618901252747},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.41638800501823425},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11737465858459473},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09125697612762451}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8603925704956055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.773081362247467},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712571620941162},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6654899716377258},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5970761775970459},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5959826707839966},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5721656084060669},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.5698987245559692},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5640138387680054},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5493495464324951},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5306474566459656},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5268104076385498},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5076388120651245},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5034844279289246},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.44660618901252747},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.41638800501823425},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11737465858459473},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09125697612762451},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-tw.2014.6904064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-tw.2014.6904064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Consumer Electronics - Taiwan","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1608462934","https://openalex.org/W1987118352","https://openalex.org/W2115528090","https://openalex.org/W2147334734","https://openalex.org/W2152473410","https://openalex.org/W2161969291","https://openalex.org/W3151111735","https://openalex.org/W6681942348"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W1977648593","https://openalex.org/W2348780717","https://openalex.org/W2780165746","https://openalex.org/W4252030899","https://openalex.org/W2561938144","https://openalex.org/W3087821047","https://openalex.org/W2071599417","https://openalex.org/W2602033589"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3],"propose":[4],"a":[5,68],"mechanism":[6,106],"to":[7,22,48,94,98],"segment":[8],"groups":[9,38],"of":[10,26,34,39,55,75,111,141],"pedestrians":[11],"using":[12,67],"hybrid":[13],"features":[14,60,78],"for":[15,63,81],"intelligent":[16],"surveillance":[17,132],"systems.":[18],"The":[19,53,104,122,134],"goal":[20],"is":[21,91,125],"specify":[23],"the":[24,30,50,95,100,139,142],"number":[25],"people":[27],"and":[28,32,43,58,84,118,131],"locate":[29],"position":[31],"size":[33],"each":[35],"individual":[36],"in":[37],"people.":[40],"Human":[41],"detection":[42,66,102],"clustering":[44,89],"techniques":[45],"are":[46,61,79],"combined":[47],"achieve":[49],"segmentation":[51,145],"purpose.":[52],"histogram":[54],"oriented":[56],"gradients":[57],"curvelet":[59],"extracted":[62],"full":[64],"body":[65,83,86],"support":[69],"vector":[70],"machine":[71],"classifier.":[72],"Modified":[73],"Haar":[74],"Oriented":[76],"Gradient":[77],"constructed":[80],"upper":[82],"lower":[85],"detectors.":[87],"A":[88],"algorithm":[90],"then":[92],"applied":[93],"detected":[96],"humans":[97],"eliminate":[99],"redundant":[101],"responses.":[103],"proposed":[105,123,143],"requires":[107],"no":[108],"prior":[109],"assumptions":[110],"human":[112,114],"sizes,":[113],"heights,":[115],"camera":[116],"distances,":[117],"other":[119],"calibration":[120],"parameters.":[121],"approach":[124],"tested":[126],"with":[127],"pedestrian":[128,144],"benchmark":[129],"dataset":[130],"videos.":[133],"experimental":[135],"results":[136],"have":[137],"demonstrated":[138],"effectiveness":[140],"mechanism.":[146]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}