20.500.12556/RUL-144883 Vizualno sledenje objektov na vgrajenih napravah Visual object tracking on embedded devices V okviru diplomskega dela je bilo implementirano in ovrednoteno delovanje vizualnega sledilnika na vgrajeni napravi Luxonis OAK-1. Izbran je bil sledilnik STARK, ki spada v družino sledilnikov, ki jih sestavljajo globoke nevronske mreže. Bolj specifično sledilnik temelji na arhitekturi transformer, ki je trenutno uporabljena v vseh najboljših vizualnih sledilnikih. Sledilnik je bilo potrebno prilagoditi ter ga prevesti v OpenVINO format, ki omogoča uporabo na vgrajeni napravi. Poleg tega je bilo potrebno zasnovati cevovod, po katerem se podatki na napravi pretakajo. Vgrajena naprava tako izvaja vse potrebne operacije od zajema slik do napovedi pozicije brez vpletenosti gostiteljskega sistema. S tem smo dosegli, da so performance sledenja neodvisne od gostiteljskega sistema. Vse verzije sledilnika smo eksperimentalno ovrednotili na testih VOT2021 in VOT2022 in s tem dokazali, da se ob prenosu na vgrajeno napravo, delovanje sledilnika ni poslabšalo. In this diploma thesis, a state-of-the-art visual tracker is implemented and evaluated on the embedded device Luxonis OAK-1. The tracker STARK is chosen, which belongs to the family of deep neural network-based trackers. More specifically the tracker uses the novel transformer neural network architecture, which is making its way into increasingly more best performing visual trackers. During the implementation process we had to modify the tracker, convert it to the OpenVINO format, which can be used for inference on the embedded device. To run the processing completely on the embedded device the correct pipeline architecture was also developed, which allows for all the processing to be executed on the embedded device and consequently allows autonomous operation of the embedded device. With this level of autonomy, we successfully decoupled tracking performance from the performance of the host system. We evaluated all versions of the tracker on the VOT2021 and VOT2022. With the evaluation we proved that the tracker ported to the embedded device doesn't lose any tracking performance. računalniški vid na vgrajenih napravah DepthAI vizualni sledilnik embedded computer vision DepthAI visual tracker true false false Slovenski jezik Angleški jezik Diplomsko delo/naloga 2023-03-20 11:40:00 2023-03-20 11:40:04 2023-03-31 06:23:27 0000-00-00 00:00:00 2023 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 35070 147286787 Princic_Nik_-_Vizualno_sledenje_objektov_na_vgrajenih_napravah.pdf Princic_Nik_-_Vizualno_sledenje_objektov_na_vgrajenih_napravah.pdf 1 2805DD9D1A8AB94A5BAAE9365633F47A 11c1aa6725317d99e7f14dd096be49c45e0553287700ff02c381bec966928abf 807e7454-c70b-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=167436 Fakulteta za računalništvo in informatiko 0 0 0