{"id":"https://openalex.org/W2196879655","doi":"https://doi.org/10.1109/isc2.2015.7366164","title":"A model based method of pedestrian abnormal behavior detection in traffic scene","display_name":"A model based method of pedestrian abnormal behavior detection in traffic scene","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2196879655","doi":"https://doi.org/10.1109/isc2.2015.7366164","mag":"2196879655"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/isc2.2015.7366164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5072615359","display_name":"Qianyin Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Qianyin","raw_affiliation_strings":["Key Laboratory of Video and Image Intelligent Analysis and Application Technology of MPS, P. R. China, Guangzhou, China","Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Video and Image Intelligent Analysis and Application Technology of MPS, P. R. China, Guangzhou, China","institution_ids":[]},{"raw_affiliation_string":"Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100752569","display_name":"Guoming Li","orcid":"https://orcid.org/0000-0001-7624-8051"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Guoming","raw_affiliation_strings":["Key Laboratory of Video and Image Intelligent Analysis and Application Technology of MPS, P. R. China, Guangzhou, China","Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Video and Image Intelligent Analysis and Application Technology of MPS, P. R. China, Guangzhou, China","institution_ids":[]},{"raw_affiliation_string":"Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102374265","display_name":"Jinwei Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113138","display_name":"Guangzhou Automobile Group (China)","ror":"https://ror.org/026fzn952","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210113138"]},{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Jinwei","raw_affiliation_strings":["Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou, China","Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou, China","institution_ids":["https://openalex.org/I4210113138"]},{"raw_affiliation_string":"Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077419230","display_name":"Xiying Li","orcid":"https://orcid.org/0000-0002-4753-8022"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Xiying","raw_affiliation_strings":["Key Laboratory of Video and Image Intelligent Analysis and Application Technology of MPS, P. R. China, Guangzhou, China","Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Video and Image Intelligent Analysis and Application Technology of MPS, P. R. China, Guangzhou, China","institution_ids":[]},{"raw_affiliation_string":"Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.271,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.870653,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":89},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9957,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9957,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9953,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9945,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.66791004},{"id":"https://openalex.org/keywords/pedestrian-crossing","display_name":"Pedestrian crossing","score":0.6604618},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4233144}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.9205494},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.66791004},{"id":"https://openalex.org/C2777819797","wikidata":"https://www.wikidata.org/wiki/Q8010","display_name":"Pedestrian crossing","level":3,"score":0.6604618},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6572381},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.63086283},{"id":"https://openalex.org/C117797892","wikidata":"https://www.wikidata.org/wiki/Q286363","display_name":"Shadow (psychology)","level":2,"score":0.60794526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.56653756},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.5130986},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4745406},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4233144},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.26652655},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18626785},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.13823041},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.07971597},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/isc2.2015.7366164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":8,"referenced_works":["https://openalex.org/W2001078957","https://openalex.org/W2003787024","https://openalex.org/W2118836531","https://openalex.org/W2124635854","https://openalex.org/W2127750920","https://openalex.org/W2128588152","https://openalex.org/W2378628385","https://openalex.org/W2388318081"],"related_works":["https://openalex.org/W650967530","https://openalex.org/W4390813505","https://openalex.org/W4388221821","https://openalex.org/W2981141433","https://openalex.org/W2972620127","https://openalex.org/W2905794575","https://openalex.org/W2538580916","https://openalex.org/W2093504595","https://openalex.org/W187110833","https://openalex.org/W122740207"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,34,79,92,152],"reduce":[3],"traffic":[4,157],"accidents":[5],"caused":[6],"by":[7,100],"the":[8,20,26,30,36,50,54,93,106,112,134,140],"pedestrian,":[9],"five":[10,37,115],"kinds":[11,38,116],"of":[12,39,114,117],"dangerous":[13,40],"pedestrian":[14,27,41,62,66,68,96,101,118,141],"abnormal":[15,42,71,119,124,142],"behaviors":[16,120,125,143],"are":[17,87,98,126],"studied":[18],"in":[19,145,154],"paper.":[21],"A":[22],"behavior":[23,72],"model":[24,113],"between":[25,108],"trajectory":[28,109],"and":[29,70,110,123,138,148],"road":[31,45,57],"is":[32,77,121,150],"built":[33],"describe":[35],"behaviors:":[43],"crossing":[44,49],"border,":[46],"illegal":[47],"stay,":[48],"road,":[51,111],"moving":[52,81],"along":[53],"curb,":[55],"entering":[56],"area.":[58],"The":[59],"method":[60,76,135],"contains":[61],"detection,":[63],"shadow":[64,84],"elimination,":[65,85],"recognition,":[67],"tracking":[69],"detection.":[73],"Background":[74],"subtraction":[75],"used":[78],"detect":[80,139],"targets.":[82],"After":[83],"pedestrians":[86],"distinguished":[88],"from":[89],"vehicles":[90],"according":[91,128],"ratio.":[94],"Then,":[95],"trajectories":[97],"gotten":[99],"tracking.":[102],"Finally,":[103],"based":[104],"on":[105],"relation":[107],"established,":[122],"detected":[127],"this":[129],"model.":[130],"Experiments":[131],"show":[132],"that":[133],"can":[136],"distinguish":[137],"effectively":[144],"short":[146],"time,":[147],"it":[149],"suitable":[151],"use":[153],"real":[155],"time":[156],"monitoring.":[158]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2196879655","counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-01-05T20:45:47.991227","created_date":"2016-06-24"}