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Identifier 000413380
Title Evaluating design options of Bag-of Visual-Words based methods for action classification
Alternative Title Αξιολόγηση σχεδιαστικών επιλογών μεθόδων βασιζόμενων σε συλλογή οπτικών λέξεων για την κατηγοριοποίηση ανθρώπινων δραστηριοτήτων
Author Μανουσάκη, Βικτωρία Ε.
Thesis advisor Αργυρός, Αντώνης
Reviewer Στεφανίδης, Κωνσταντίνος
Ζαμπούλης, Ξενοφών
Abstract In recent years, the problems of vision-based human motion analysis and action classification/recognition have attracted a lot of attention due to the significance of their solution in domains such as assisted living, surveillance, humancomputer/ robot interaction, etc. Despite several breakthroughs, human action recognition remains a challenging problem that is unsolved in its generality. In this work, we are interested in action classification based on motion capture/ skeletal data and we rely on the Bags of Visual Words (BoVWs) features encoding. We follow an action classification framework consisting of three main steps: (a) feature extraction, (b) representation/encoding based on a BoVWs codebook and (c) classification of the resulting action representations. In this study, our goal is to provide an experimental evaluation of various options regarding the selection of the components of this framework that, when instantiated, give rise to a specificaction classification method. In that direction, the contributions of this work are manyfold. First, we investigate the performance of three existing types of 3D skeletal features. We also propose a new feature for representing human 3D pose data that is inspired by the work on Motion Boundary Histograms (MBH). The use of the proposed feature is shown to produce results that are competitive to the state of the art. We explore three different classification methods (K-Nearest Neighbors, Support Vector Machines, Radial Basis Function Neural Networks). We also investigate the size of the codebook used to represent actions, which is a major design issue in BoVW-based methods. To achieve this, we perform an empirical, almost exhaustive study to determine the best codebook size for each feature type and classifier. Most of the previous works define a specific codebook size without providing details on how this has been decided. In contrast, we explore methods that determine automatically the codebook size. This investigation shows that Afinity Propagation, an unsupervised clustering technique that determines automatically the number of clusters in a dataset, can be used effectively as a replacement of the k-Means algorithm which is used in most of the BoVW-based recognition methods. Additionally, we explore feature encoding alternatives to BoWs such as the Bag of Temporal Words (BoTW) and the Vector of Locally Aggregated Descriptors (VLAD). The obtained results show that the simple BoVW encoding outperforms these more complicated choices. All methods and action classification design choices have been evaluated quantitatively based on a series of experiments that have been carried out on the standard, extensive and ground truth-annotated Berkeley MHAD dataset.
Language English
Subject KNN
Issue date 2017-11-24
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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