Adaptive Objects Tracking by Using Statistical Features Shape Modeling and Histogram Analysis

Abstract

We propose a novel method for object tracking using an adaptive algorithm based on statistical analysis of objects shape. To track objects in video sequence, we use a sys-tem that combines two algorithms: a histogram analysis algorithm and a statistical shape features modeling algorithm. The main improvement of the proposed system with respect to the others present in literature is that we do nonuse any a priori knowledge about how objects look like.This no apriori model has been carried out by computinga model that takes into account the statistical behavior of the most important objects features over the whole video frames. Moreover, an adaptive mechanism allows us tore set the statistical model creation when such a model is too much dissimilar from the real blobs features. Experiments on some real world dif¿cult scenarios of low resolu-tion videos and in unconstrained environments demonstrate the very promising results achieved.