{"id":"https://openalex.org/W1971261573","doi":"https://doi.org/10.1145/2808492.2808526","title":"A patch-based affinity measurement method for online multiple pedestrian tracking","display_name":"A patch-based affinity measurement method for online multiple pedestrian tracking","publication_year":2015,"publication_date":"2015-08-19","ids":{"openalex":"https://openalex.org/W1971261573","doi":"https://doi.org/10.1145/2808492.2808526","mag":"1971261573"},"language":"en","primary_location":{"id":"doi:10.1145/2808492.2808526","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2808492.2808526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Internet Multimedia Computing and Service","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/A5026744983","display_name":"Feidie Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feidie Liang","raw_affiliation_strings":["Xinhua News Agency, Beijing, China","Xinhua News Agency, Beijing, China#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinhua News Agency, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"Xinhua News Agency, Beijing, China#TAB#","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056776177","display_name":"Sheng Tang","orcid":"https://orcid.org/0000-0003-3573-2407"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Tang","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China","Chinese Academy of Sciences , Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]},{"raw_affiliation_string":"Chinese Academy of Sciences , Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086614110","display_name":"Rui Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rui Lv","raw_affiliation_strings":["Xinhua News Agency, Beijing, China","Xinhua News Agency, Beijing, China#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinhua News Agency, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"Xinhua News Agency, Beijing, China#TAB#","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":0.9993000030517578,"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":0.9993000030517578,"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":0.9986000061035156,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9879999756813049,"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/computer-science","display_name":"Computer science","score":0.757390022277832},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6185689568519592},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6092696785926819},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5992904901504517},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5684275031089783},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.556365430355072},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5104876756668091},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46657881140708923},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37281689047813416},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3701762557029724},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3533894121646881},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10688784718513489}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.757390022277832},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6185689568519592},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6092696785926819},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5992904901504517},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5684275031089783},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.556365430355072},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5104876756668091},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46657881140708923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37281689047813416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3701762557029724},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3533894121646881},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10688784718513489},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2808492.2808526","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2808492.2808526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Internet Multimedia Computing and Service","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W115730240","https://openalex.org/W1997500560","https://openalex.org/W2035153336","https://openalex.org/W2053181242","https://openalex.org/W2056250745","https://openalex.org/W2082716591","https://openalex.org/W2086627757","https://openalex.org/W2113577207","https://openalex.org/W2113697988","https://openalex.org/W2120544328","https://openalex.org/W2122469558","https://openalex.org/W2134529534","https://openalex.org/W2134905085","https://openalex.org/W2148958980","https://openalex.org/W2161969291","https://openalex.org/W3177525997"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2392100589","https://openalex.org/W2263699433","https://openalex.org/W2512789322","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2392921965","https://openalex.org/W2358755282","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"The":[0],"accuracy":[1],"of":[2,11,60,66,77,91],"affinity":[3,23,51,71],"measurement":[4,24],"is":[5,37,89],"one":[6],"determining":[7],"factor":[8],"for":[9,32],"performance":[10],"detection-based":[12],"multiple":[13],"pedestrian":[14],"tracking":[15],"methods.":[16,137],"To":[17],"get":[18],"high":[19],"accuracy,":[20],"most":[21],"existing":[22],"techniques":[25],"require":[26],"expensive":[27],"and":[28,55,58,75,94,101,114,130],"laborious":[29],"data":[30,92,128],"annotation":[31,93],"model":[33],"training":[34],"when":[35],"there":[36],"no":[38],"future":[39,95],"information":[40,103],"in":[41,69,82,106],"online":[42,80],"applications.":[43],"In":[44,97],"this":[45],"study,":[46],"we":[47],"propose":[48],"to":[49,62],"measure":[50],"based":[52],"on":[53,125],"patches":[54,61,68,78],"use":[56],"discriminability":[57,74],"representability":[59,76],"describe":[63],"different":[64,67],"importance":[65],"the":[70],"calculation.":[72],"Both":[73],"are":[79,104],"learned":[81],"an":[83],"unsupervised":[84],"way,":[85],"so":[86,109],"our":[87,107,123],"method":[88],"free":[90],"information.":[96],"addition,":[98],"motion,":[99],"size":[100],"time":[102],"explored":[105],"method,":[108],"entering/leaving":[110],"pedestrians,":[111],"short-term":[112],"occlusions":[113],"missing":[115],"detections":[116],"can":[117],"be":[118],"partially":[119],"overcome.":[120],"We":[121],"evaluate":[122],"approach":[124],"several":[126],"public":[127],"sets":[129],"show":[131],"significant":[132],"improvements":[133],"compared":[134],"with":[135],"state-of-the-art":[136]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
