{"id":77658,"date":"2020-04-12T19:42:20","date_gmt":"2020-04-12T17:42:20","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/fairmot-sistema-dlya-bystrogo-otslezhivaniya-neskolkih-obektov-na-video"},"modified":"2020-04-12T19:42:20","modified_gmt":"2020-04-12T17:42:20","slug":"fairmot-sistema-dlya-bystrogo-otslezhivaniya-neskolkih-obektov-na-video","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/news\/fairmot-sistema-dlya-bystrogo-otslezhivaniya-neskolkih-obektov-na-video","title":{"rendered":"FairMOT, s\u00fcsteem mitme objekti kiireks j\u00e4lgimiseks videos","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Microsofti ja Kesk-Hiina \u00dclikooli teadlased arendasid<\/a><\/noindex> uus k\u00f5rge j\u00f5udlusega meetod mitme objekti j\u00e4lgimiseks videos, kasutades masin\u00f5ppe tehnoloogiaid \u2014 FairMOT (Fair Multi-Object Tracking). Kood meetodi rakendamiseks Pytorch'i baasil ja treenitud mudelid. avaldatud<\/a><\/noindex> on GitHub. <\/p>\n

Enamik olemasolevaid objekti j\u00e4lgimise meetodeid kasutab kahte etappi, millest iga\u00fcks on rakendatud eraldi n\u00e4rviv\u00f5rgus. Esimeses etapis tuvastatakse huvipakkuvate objektide asukohad, samas kui teises etapis kasutatakse assotsiatsioonide otsimise mudelit objektide uue identifitseerimise ja ankurduste seostamiseks. <\/p>\n

FairMOT-is rakendatakse \u00fches etapis deformeeritavat konvolutsiooniv\u00f5rku (DCNv2<\/a><\/noindex>, Deformable Convolutional Network), mis v\u00f5imaldab t\u00f5husamat objekti j\u00e4lgimise kiiruset\u00f5usu. FairMOT t\u00f6\u00f6tab ilma ankurdusteta, kasutades uuesti identifitseerimise mehhanismi objektide keskpunktide nihke m\u00e4\u00e4ramiseks k\u00f5rgelt t\u00e4pses objektikaardis. Samal ajal t\u00f6\u00f6tab t\u00f6\u00f6tleja, mis hindab objektide individuaalseid jooni, mida saab kasutada nende identiteedi ennustamiseks, samas kui p\u00f5hikomponent kogub eelnimetatud jooni erineva suurusega objektide manipuleerimiseks.
\n

\"FairMOT,<\/center><\/p>\n

FairMOT mudeli koolitamiseks kasutati kuue avaliku andmestiku kombinatsiooni, et tuvastada ja otsida inimesi (ETH, CityPerson, CalTech, MOT17, CUHK-SYSU). Mudelit testiti videovaliku kontrollimiseks 2DMOT15<\/a><\/noindex>, MOT16<\/a><\/noindex>, MOT17<\/a><\/noindex> ja MOT20<\/a><\/noindex>, mille on esitanud projekt MOT Challenge<\/a><\/noindex> ja mis h\u00f5lmab erinevaid olukordi, kaameraliikumist v\u00f5i p\u00f6\u00f6rlemist, erinevaid vaatepunkte. Teostatud testimine n\u00e4itas, et
\nFairMOT \u00fcletamas<\/a><\/noindex> on kiireim konkurentsiv\u00f5imeline mudel TrackRCNN<\/a><\/noindex> ja JDE<\/a><\/noindex> videote testimisel, mille kaadrisagedus on 30 kaadrit sekundis, n\u00e4idates j\u00f5udlust, mis on piisav tavavideovoogude reaalajas anal\u00fc\u00fcsimiseks. <\/p>\n

<\/a><\/noindex><\/p>\n

Allikas: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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