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Uporaba računalniškega vida in algoritmov globokega učenja za samodejno zaznavanje vrat in žoge na rokometnem igrišču
ID Struna, Denis (Author), ID Perš, Janez (Mentor) More about this mentor... This link opens in a new window, ID Ivanovska, Marija (Comentor)

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Abstract
V okviru diplomskega dela je predstavljen računalniški algoritem za avtomatsko analizo slik iz videoposnetkov rokometnih tekem, pridobljenih iz kanala Youtube Rokometne zveze Slovenije. Predstavljeni pristop vključuje detekcijo dveh različnih objektov - rokometne žoge in vrat, kar predstavlja osnovo za napredno, računalniško podprto analizo rokometne tekme. Začetni korak pristopa je obsegal izvoz slik iz videoposnetkov ter oblikovanje baze slik in ročno označevanje žoge in vrat na igrišču. Končni rezultat je baza slik iz rokometnih tekem z označbami v formatu COCO. Tako pripravljena baza je bila v nadaljevanju uporabljena za učenje algoritma DETR za razpoznavanje označenih objektov. Pri testiranju naučenega algoritma je bilo ugotovljeno, da je DETR zmožen detektirati vrata brez napak, medtem ko je bila detekcija žoge manj zanesljiva in močno odvisna od stopnje zamegljenosti objekta, ki se pojavi kot posledica hitrosti gibanja žoge.

Language:Slovenian
Keywords:računalniški vid, rokomet, baza rokometnih slik, detekcija objektov, DETR
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FE - Faculty of Electrical Engineering
Year:2024
PID:20.500.12556/RUL-159391 This link opens in a new window
COBISS.SI-ID:201178627 This link opens in a new window
Publication date in RUL:09.07.2024
Views:202
Downloads:47
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Secondary language

Language:English
Title:Computer Vision and Deep Learning for Automatic Goal and Ball Detection on a Handball Court
Abstract:
The thesis presents a computer algorithm for automatic image analysis of handball match videos obtained from the YouTube channel of the Handball Federation of Slovenia. The presented approach involves the detection of two different objects - the ball and the goal on the playground, which represents the basis for an advanced, computer-aided analysis of handball matches. The initial step of the approach consisted of exporting images from video clips, creating an image database. Balls and goals present in the images were then manually annotated following the formatting style of the widely used dataset for object detection COCO. The prepared database was then used to train DETR to detect labelled objects. In the testing phase, it was found that DETR was able to detect the goals without errors. The detection of the balls was on the other hand less accurate and strongly dependent on the intensity of blurriness of the objects, which occurs as a consequence of the ball's speed of movement.

Keywords:computer vision, handball, handball database, object detection, DETR

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