Metode za brojanje ljudi u slici

master's thesis

• Title (english): Methods for Counting People in Images
• Author: Luka Novak
• Mentor: Sven Lončarić (mentor)
• Committee member: Sven Lončarić (predsjednik povjerenstva)
• Committee member: Marko Subašić (član povjerenstva)
• Committee member: Tomislav Petković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2018-07-03, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U ovom radu prikazana je ukratko povijest računalnog vida s fokusom na duboke konvolucijske neuronske mreže. Napravljena su i testirana dva sustava za detekciju i praćenje ljudi na slici i videu: prvi je Viola-Jones algoritam temeljen na kaskadama Haarovih značajki, te YOLO (You Only Look Once) algoritam, jedan od trenutno najboljih algoritama za detekciju objekata temeljen na konvolucijskim neuronskim mrežama. Na kraju su opisani dobiveni rezultati te su analizirani krivo prepoznati uzorci i situacije.

Abstract (english)

In this work, brief history of computer vision is shown. The focus is on the deep convolutional neural networks. Two systems for people detection and tracking are built and tested: first one is Viola-Jones algorithm based on Haar features cascades, and YOLO (You Only Look Once) algorithm, one of the best state-of-the-art algorithms for object detection based on deep convolutional neural networks. Following that, numerical results are expressed from the classifiers, their performance is evaluated and misclassified examples and situations are briefly discussed.

• Keywords: neuronske mreže; duboke neuronske mreže; konvolucijske neuronske mreže; računalni vid; raspoznavanje uzoraka; duboko učenje; Viola-Jones algoritam; Haarove kaskade; YOLO; praćenje trajektorija
• Keywords (english): neural networks; deep neural networks; convolutional neural networks; computer vision; pattern recognition; deep learning; Viola-Jones algorithm; Haar cascades; YOLO; trajectories tracking
• Language: croatian
• URN:NBN: urn:nbn:hr:168:200788
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Repository: FER Repository
• Created on: 2019-03-13 23:25:20