Please use this identifier to cite or link to this item: http://dspace.lib.uom.gr/handle/2159/25644
Author: Τζόγκα, Χριστίνα
Title: Addressing Computer Vision Challenges using an Active Learning Framework
Alternative Titles: Addressing Computer Vision Challenges
Date Issued: 2021
Department: Πρόγραμμα Μεταπτυχιακών Σπουδών στην Τεχνητή Νοημοσύνη και Αναλυτική Δεδομένων
Supervisor: Ρεφανίδης, Ιωάννης
Abstract: Machine Learning applications has transformed everyday life as well as industry by providing new successful opportunities in healthcare, transportation, banking, security, media monitoring and more. Computer Vision is an application of Machine Learning that recently has done a lot of progress, particularly in Face Recognition and Object Detection systems. These systems require large data sets to be trained with. Nevertheless, the available data sets contain large amounts of unlabelled samples. Active Learning is an innovative field that addresses the challenge of labelling large sets of unlabelled samples by leveraging only a small amount of manually labelled data. An efficient way of labelling a small amount of training data is utilizing user-friendly annotation tools. The latter allow playing a whole video streaming and capturing the desired entities. This interactive method could be very efficient as well as time-saving in comparison to traditional data collection methods. This thesis builds on state-of-the-art Face Recognition and Object Detection models, by implementing optimization methods that enhance the recognition accuracy. Further training is being introduced by making use of a robust Active Learning framework that results in creating extended data sets. Finally, our thesis proposes an integrated system, which involves effective techniques of associating face and object identification informa- tion, in order to extract as much knowledge as possible from a video streaming, in real-time.
Keywords: Face Recognition
Object Detection
Active Learning
Deep Learning
Data Set
Information: Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2021.
Rights: Αναφορά Δημιουργού 4.0 Διεθνές
Appears in Collections:ΠΜΣ στην Τεχνητή Νοημοσύνη και Αναλυτική Δεδομένων (Μ)

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