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Discriminative correlation filter with segmentation and context for robust tracking
ID Lampe, Ajda (Author), ID Kristan, Matej (Mentor) More about this mentor... This link opens in a new window

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Abstract
Visual object tracking is an area in the field of computer vision, which has seen great popularity increase due to a large availability of video data. There are many different tracking tasks, such as multiple object tracking, long-term tracking and specialized trackers, expected to perform well in a very specific domain. In this work, we focus on online generic short-term single object tracking, which can be considered the base visual tracking task and can be adaptable to any of the previously mentioned tasks. We propose a new tracker, based on correlation filtering, augmented with context information and a predicted object segmentation mask. The results on benchmarks fall far behind the current state-of-the-art, however the proposed method consistently outperforms baseline trackers, which shows the methods potential for future improvements.

Language:English
Keywords:computer vision, visual object tracking, tracking by detection, correlation, segmentation
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FRI - Faculty of Computer and Information Science
Year:2020
PID:20.500.12556/RUL-121958 This link opens in a new window
COBISS.SI-ID:40159235 This link opens in a new window
Publication date in RUL:12.11.2020
Views:1076
Downloads:247
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Secondary language

Language:Slovenian
Title:Diskriminativni korelacijski filter s segmentacijo in uporabo konteksta za robustno sledenje
Abstract:
Vizualno sledenje objektom je področje računalniškega vida, ki je v zadnjih letih doživelo velik razcvet, zahvaljujoč dostopnosti video vsebin. Problem lahko razdelimo na več podnalog, na primer sledenje več objektom, dolgoročno sledenje ali specializirano sledenje za točno določeno domeno. V tem delu se omejimo na splošne kratkoročne sledilnike, ki sledijo enemu objektu. To lahko namreč razumemo kot najbolj osnovno nalogo vizualnega sledenja, ki jo lahko razširimo za delovanje na prej omenjenih problemih. V delu predstavimo nov sledilnik, ki temelji na sledenju s korelacijskimi filtri, razširimo pa ga z uporabo kontekstne informacije in segmentacijske maske. V primerjavi z ostalimi sledilniki predlagana metoda sicer ne dosega rezultatov, primerljivih z najmodernejšimi sledilniki, vendar pa dosledno dosega boljše rezultate od osnovnejših sledilnikov, kar kaže na potencial metode za nadaljnje izboljšave.

Keywords:računalniški vid, vizualno sledenje objektom, sledenje z detekcijo, korelacija, semantična segmentacija

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