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

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 system 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 not use any a priori knowledge about how objects look like. This no a-priori model has been carried out by computing a model that takes into account the statistical behaviour of the most important objects features over the whole video frames. Moreover, an adaptive mechanism allows us to reset the statistical model creation when such a model is too much dissimilar from the real blobs features. Experiments on some real-world difficult scenarios of low resolution videos and in unconstrained environments demonstrate the very promising results achieved.