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.
Highlights
In the context of monitoring system, we describe a method for images processing and analysing
Object Tracking is an important task in computer vision applications including video surveillance, Radar, robot navigation
This is a challenging task because these images contain significant distortions because of mirror geometry
Summary
In the context of monitoring system, we describe a method for images processing and analysing. Object Tracking is an important task in computer vision applications including video surveillance, Radar, robot navigation This work defines this process in a omnidirectional images sequence. The omnidirectional image provided by a camera with a Single View Point [(SVP) (Geyer and Daniilidis, 2000; Barreto and Araujo, 2001; Chiuso and Picci, 1998)] is modelled by a spherical image (Fig. 1). This model called unified projection model and was introduced in (Geyer and Daniilidis, 2000) and used in (Baker and Nayar, 1998).
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