Research Article Open Access

Spherical Object Tracking using Von Mises-Fisher Distribution in Catadioptric System

Khald Anisse1, Radgui Amina2 and Rziza Mohamed1
  • 1 Mohamed V University, Morocco
  • 2 National Institute of Posts and Telecommunications, Morocco

Abstract

Tracking objects on video sequences is a very challenging task in computer vision applications. However, there is few articles that deal with this topic in catadioptric vision. This paper describes a new approach of omnidirectional images (gray level) processing based on inverse stereographic projection in the image plane. Our work is based on minimizing the distance between two models. The model named von Mises-Fisher distribution have as input the gabor phase and the measure used is Kullback–Leibler Divergence (KLD). In one hand, this model matching respect the deformed geometry of omnidirectional images due to using the spherical neighbourhood. In the other hand, the simulation results show that our approach gives as better performance in terms of overlapping estimation.

Journal of Computer Science
Volume 16 No. 9, 2020, 1229-1236

DOI: https://doi.org/10.3844/jcssp.2020.1229.1236

Submitted On: 16 August 2020 Published On: 20 September 2020

How to Cite: Anisse, K., Amina, R. & Mohamed, R. (2020). Spherical Object Tracking using Von Mises-Fisher Distribution in Catadioptric System. Journal of Computer Science, 16(9), 1229-1236. https://doi.org/10.3844/jcssp.2020.1229.1236

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Keywords

  • Object Tracking
  • Gabor Phase
  • Omnidirectional Image
  • Model Matching
  • VonMises-Fisher Distribution
  • Kullback–Leibler Divergence