diplomsko delo
Anže Kovač (Author), Aleš Leonardis (Mentor)

Abstract

Zaznavanje in sledenje ljudem v sistemih z več kamerami

Keywords

ralunalniški vid;mešanica gaussov;homografija;glavna os človeka;računalništvo;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [A. Kovač]
UDC: 004.93(043.2)
COBISS: 7056212 Link will open in a new window
Views: 861
Downloads: 230
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: English
Secondary title: [Detection and tracking people using multiple cameras]
Secondary abstract: One of the most interesting areas of research in computer vision is segmentation and tracking of people using monocular or multi-view systems. In this thesis we present and implement a tracker, which is capable to detect and track people using multiple cameras. Algorithm is incrementaly building a model called mixture of gaussians for each pixel independently. If the current observation does not match its model, then the appropriate pixel is marked as a foreground object (person). From those pixels we create a color representation for each foreground object. Considering color models and probable positions of the people, we track those people across the current scene. To precisely determine the ground location of a person, we map vertical axis of the person (principal axis) to a top-view plane by using homographies. The results show that this approach performs effectively when tracking individual person. However some problems are observed in situations where we monitor several occluded people in a cluttered scene.
Secondary keywords: computer vision;mixture of gaussians;homography;principal axis;computer science;diploma;
File type: application/pdf
Type (COBISS): Undergraduate thesis
Thesis comment: Univerza v Ljubljani, Fakulteta za računalništvo in informatiko
Pages: 54 str.
ID: 23868227