A computationally efficient importance sampling tracking algorithm

A computationally efficient importance sampling tracking algorithm

This paper proposes a computationally efficient importance sampling algorithm applicable to computer vision tracking. The algorithm is based on the CONDENSATION algorithm, but it avoids expensive operations that are costly in real-time embedded systems. It also includes a method that reduces the num...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Machine vision and applications 2014-10, Vol.25 (7), p.1761-1777
Hauptverfasser: Farah, Rana, Gan, Qifeng, Langlois, J. M. Pierre, Bilodeau, Guillaume-Alexandre, Savaria, Yvon
Format: Artikel
Sprache:eng
Schlagworte:
Acceleration
Algorithms
Communications Engineering
Computational efficiency
Computer Science
Computer vision
Condensation
Embedded systems
Image Processing and Computer Vision
Importance sampling
Markov analysis
Monte Carlo simulation
Networks
Original Paper
Pattern Recognition
Resampling
Sampling techniques
Tracking
Vision systems
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper proposes a computationally efficient importance sampling algorithm applicable to computer vision tracking. The algorithm is based on the CONDENSATION algorithm, but it avoids expensive operations that are costly in real-time embedded systems. It also includes a method that reduces the number of particles during execution and a new resampling scheme. Our experiments demonstrate that the proposed algorithm is as accurate as the CONDENSATION algorithm. Depending on the processed sequence, the acceleration with respect to CONDENSATION can reach 7 × for 50 particles, 12 × for 100 particles and 58 × for 200 particles.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-014-0630-5