{"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\/it\/blog\/news\/fairmot-sistema-dlya-bystrogo-otslezhivaniya-neskolkih-obektov-na-video","title":{"rendered":"FairMOT, sistema per il tracciamento veloce di pi\u00f9 oggetti nei video","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

I ricercatori di Microsoft e dell'Universit\u00e0 Centrale della Cina hanno sviluppato<\/a><\/noindex> un nuovo metodo ad alte prestazioni per il tracciamento di pi\u00f9 oggetti in video utilizzando tecnologie di apprendimento automatico \u2014 FairMOT (Fair Multi-Object Tracking). Codice con l'implementazione del metodo basato su Pytorch e modelli addestrati. pubblicati<\/a><\/noindex> su GitHub. <\/p>\n

La maggior parte degli attuali metodi di tracciamento degli oggetti utilizza due fasi, ciascuna delle quali \u00e8 implementata da una rete neurale separata. Nella prima fase, un modello identifica la posizione degli oggetti di interesse; nella seconda fase, viene utilizzato un modello di associazione per la re-identificazione degli oggetti e il collegamento con gli anchor. <\/p>\n

In FairMOT si applica un'implementazione a singola fase basata su una rete neurale convoluzionale deformabile (DCNv2<\/a><\/noindex>, Rete Convoluzionale Deformabile), che consente di ottenere un notevole incremento nella velocit\u00e0 di tracciamento degli oggetti. FairMOT funziona senza utilizzare anchor, impiegando un meccanismo di re-identificazione per determinare le variazioni nei centri degli oggetti su una mappa oggetti ad alta precisione. Parallelamente, un elaboratore valuta caratteristiche individuali degli oggetti, che possono essere utilizzate per prevedere la loro identit\u00e0, mentre il modulo principale sintetizza queste caratteristiche per la manipolazione di oggetti di diverse dimensioni.
\n

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

Per addestrare il modello in FairMOT \u00e8 stata utilizzata una combinazione di sei set di dati pubblici per la rilevazione e ricerca di persone (ETH, CityPerson, CalTech, MOT17, CUHK-SYSU). Il modello \u00e8 stato testato utilizzando raccolte video di verifica 2DMOT15<\/a><\/noindex>, MOT16<\/a><\/noindex>, MOT17<\/a><\/noindex> e MOT20<\/a><\/noindex>, fornite dal progetto MOT Challenge<\/a><\/noindex> e che coprono diverse situazioni, movimento o rotazione della camera, angoli di vista differenti. I test condotti hanno dimostrato che
\nFairMOT x264 e libvpx-vp9 in termini di compressione, ma a causa della complessit\u00e0 degli algoritmi<\/a><\/noindex> \u00e8 pi\u00f9 veloce rispetto ai modelli concorrenti TrackRCNN<\/a><\/noindex> e JDE<\/a><\/noindex> quando testato su video a 30 frame al secondo, dimostrando prestazioni sufficienti per analizzare flussi video normali in tempo reale. <\/p>\n

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

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

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