diplomsko delo
Tina Strgar (Avtor), Matej Kristan (Mentor)

Povzetek

Metoda za dolgoročno vizualno sledenje z značilnimi točkami

Ključne besede

računalniški vid;dolgoročno vizualno sledenje;dinamično učenje;posplošena Houghova transformacija;afina preslikava;računalništvo;računalništvo in informatika;univerzitetni študij;diplomske naloge;

Podatki

Jezik: Slovenski jezik
Leto izida:
Tipologija: 2.11 - Diplomsko delo
Organizacija: UL FRI - Fakulteta za računalništvo in informatiko
Založnik: [T. Strgar]
UDK: 004.93(043.2)
COBISS: 1536123075 Povezava se bo odprla v novem oknu
Št. ogledov: 59
Št. prenosov: 18
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Angleški jezik
Sekundarni naslov: A key-point based approach for long-term visual tracking
Sekundarni povzetek: In the thesis the problem of long-term visual tracking is addressed. The main challenges of the problem are on-line learning of the target's visual appearance, recognition of target's absence and it's redetection. A part-based tracker is proposed using local features and affine transformation. Long-term tracking is performed with tracking-by-detection, supported by optical flow in the short term. Two nested methods are used when fitting the transformation: firstly, a cluster of potential target points is defined, then the affine deformation is robustly estimated. New model features are added based on the global shape template, that is updated by the features themselves, forming a feedback-loop. The tracker is tested on two groups of sequences, the first targeting long-term and the second short-term trackers. The results are compared with the state-of-the-art methods. The performance of the tracker is comparable, though the problem of redetection should be more carefully addressed.
Sekundarne ključne besede: computer vision;long-term visual tracking;online learning;general Hough transform;affine transformation;computer science;computer and information science;diploma;
Vrsta datoteke: application/pdf
Vrsta dela (COBISS): Diplomsko delo/naloga
Študijski program: 1000468
Komentar na gradivo: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Strani: 52 str.
ID: 8739471