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Adaptive Template Matching Model for Object Tracking in Catadioptric System
Abstract
The objects tracking applications are ubiquitous and largely discussed to address the problem of model object tracking in images sequence. Then we propose an adapted model object over the omnidirectional image provided by the catadioptric system, using the multiscale processing (MS). This processing based on Generalized Gaussian Density (GGD) model and Kullback-Leibler Distance (KLD) take into account the deformed geometry in those images. The aim of this work is to divide an image into a number of blocks, to model these blocks by GGD parameters and computing the similarity using the KLD between the current image block that contains the object and the candidate blocks. The tracking performance of the proposed system is further improved using Spatial Overlapping Estimation (SOE), the ROC curve. Experimental results show that the proposed approach is efficient for object tracking paradigm by comparison with the classical BMM and CAM-Shift Algorithms.
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