{"id":"https://openalex.org/W3193822732","doi":"https://doi.org/10.1109/iccta40200.2016.9512942","title":"Discriminative EdgeBoxes for Action Recognition","display_name":"Discriminative EdgeBoxes for Action Recognition","publication_year":2016,"publication_date":"2016-10-25","ids":{"openalex":"https://openalex.org/W3193822732","doi":"https://doi.org/10.1109/iccta40200.2016.9512942","mag":"3193822732"},"language":"en","primary_location":{"id":"doi:10.1109/iccta40200.2016.9512942","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccta40200.2016.9512942","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 26th International Conference on Computer Theory and Applications (ICCTA)","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/A5084279369","display_name":"Mohammed El-Masry","orcid":null},"institutions":[{"id":"https://openalex.org/I59272784","display_name":"Arab Academy for Science, Technology, and Maritime Transport","ror":"https://ror.org/0004vyj87","country_code":"EG","type":"education","lineage":["https://openalex.org/I59272784"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Mohammed El-Masry","raw_affiliation_strings":["Dept. of Computer Sciences, Arab Academy for Science, Technology & Maritime Transport, Cairo, Egypt"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Sciences, Arab Academy for Science, Technology & Maritime Transport, Cairo, Egypt","institution_ids":["https://openalex.org/I59272784"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015947297","display_name":"Mohamed Waleed Fakhr","orcid":"https://orcid.org/0000-0001-5147-2639"},"institutions":[{"id":"https://openalex.org/I59272784","display_name":"Arab Academy for Science, Technology, and Maritime Transport","ror":"https://ror.org/0004vyj87","country_code":"EG","type":"education","lineage":["https://openalex.org/I59272784"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Mohamed Waleed Fakhr","raw_affiliation_strings":["Dept. of Computer Sciences, Arab Academy for Science, Technology & Maritime Transport, Cairo, Egypt"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Sciences, Arab Academy for Science, Technology & Maritime Transport, Cairo, Egypt","institution_ids":["https://openalex.org/I59272784"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084279369"],"corresponding_institution_ids":["https://openalex.org/I59272784"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29650763,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"64","issue":null,"first_page":"39","last_page":"44"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9984999895095825,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.900062084197998},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7795292139053345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7543137073516846},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6049259901046753},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.6020247936248779},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6007667183876038},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.552819550037384},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5087984204292297},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4970867931842804},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.48319852352142334},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4217971861362457},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3243938088417053},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.15604573488235474}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.900062084197998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7795292139053345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7543137073516846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6049259901046753},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.6020247936248779},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6007667183876038},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.552819550037384},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5087984204292297},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4970867931842804},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.48319852352142334},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4217971861362457},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3243938088417053},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.15604573488235474}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccta40200.2016.9512942","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccta40200.2016.9512942","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 26th International Conference on Computer Theory and Applications (ICCTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7599999904632568,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W136504859","https://openalex.org/W1489737693","https://openalex.org/W1534763723","https://openalex.org/W1590510366","https://openalex.org/W1595717062","https://openalex.org/W1796848575","https://openalex.org/W1871385855","https://openalex.org/W1909538523","https://openalex.org/W1976047850","https://openalex.org/W1983705368","https://openalex.org/W1988192097","https://openalex.org/W1993229407","https://openalex.org/W1998605630","https://openalex.org/W2002706836","https://openalex.org/W2009285864","https://openalex.org/W2020163092","https://openalex.org/W2024868105","https://openalex.org/W2055753778","https://openalex.org/W2063153269","https://openalex.org/W2066941820","https://openalex.org/W2068611653","https://openalex.org/W2075386676","https://openalex.org/W2095661305","https://openalex.org/W2100916003","https://openalex.org/W2104446196","https://openalex.org/W2105101328","https://openalex.org/W2106996050","https://openalex.org/W2108333036","https://openalex.org/W2118877769","https://openalex.org/W2126574503","https://openalex.org/W2135658380","https://openalex.org/W2139594308","https://openalex.org/W2153635508","https://openalex.org/W2164688768","https://openalex.org/W2533739470","https://openalex.org/W6638233703","https://openalex.org/W6648464962","https://openalex.org/W6677548441","https://openalex.org/W6683994797"],"related_works":["https://openalex.org/W4237171675","https://openalex.org/W3036286480","https://openalex.org/W4287027631","https://openalex.org/W3192357901","https://openalex.org/W2387360586","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W4390524233"],"abstract_inverted_index":{"Due":[0],"to":[1,26,41,47,61,98,130,160],"the":[2,28,77,151,155,167],"huge":[3],"amount":[4,51],"of":[5,52,57,157],"online":[6],"videos":[7],"uploaded":[8],"and":[9,105,124,150],"viewed":[10],"every":[11],"day,":[12],"there":[13],"is":[14,60],"an":[15,84],"emerging":[16],"need":[17,25,40],"nowadays":[18],"for":[19,67,121],"action":[20],"recognition":[21,164],"techniques.":[22,169],"These":[23,74],"techniques":[24],"consider":[27],"large":[29],"variations":[30],"in":[31],"camera":[32],"motion,":[33],"viewpoint,":[34],"cluttered":[35],"background,":[36],"etc.":[37],"Moreover,":[38],"they":[39],"be":[42],"unsupervised":[43,65],"or":[44],"weakly":[45],"supervised":[46],"can":[48,82],"absorb":[49],"such":[50],"different":[53],"actions.":[54],"The":[55],"goal":[56],"this":[58,88],"paper":[59],"introduce":[62],"a":[63,92,145],"new":[64],"technique":[66,93],"mining":[68],"mid-level":[69],"discriminative":[70,119],"patches":[71,75],"from":[72,95],"videos.":[73],"are":[76],"most":[78],"representative":[79],"parts":[80],"that":[81],"describe":[83],"action.":[85],"To":[86],"achieve":[87,161],"goal,":[89],"we":[90,117],"generalize":[91],"borrowed":[94],"2D":[96],"images":[97],"generate":[99],"bounding":[100],"boxes":[101,129],"with":[102],"high":[103],"motion":[104],"appearance":[106],"saliencies":[107],"then":[108],"apply":[109],"iterative":[110],"clustering/classification":[111],"procedure":[112],"on":[113,133,144],"generated":[114],"boxes.":[115,139],"Then,":[116],"calculate":[118],"score":[120],"each":[122],"box":[123],"finally":[125],"select":[126],"top":[127],"ranked":[128],"train":[131],"Exemplar-SVM":[132],"low-level":[134],"features":[135],"extracted":[136],"inside":[137],"selected":[138],"We":[140],"evaluate":[141],"our":[142,158],"approach":[143,159],"challenging":[146],"dataset":[147],"namely":[148],"YouTube":[149],"experimental":[152],"results":[153],"demonstrate":[154],"effectiveness":[156],"better":[162],"average":[163],"accuracy":[165],"than":[166],"state-of-the-art":[168]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
