Optimization of the SSD multiple kernel tracking applied to IR video sequences

摘要

This paper addresses the problem of tracking a target in an IR video sequence using a kernel based histogram representation of the target. In this field, gradient ascent methods have demonstrated useful results with weighted kernels and in particular Mean Shift is currently the most commonly used gradient scale method. Our approximation follows the work made by Hager, that uses a SSD objective function (derived from Matusita metric) and combines it with a Newton-like maximization method, resulting a fast gradient scale system. An important property is that this method enables the use of multiple kernels, allowing a more powerful representation with a minimum increasing of computational cost. We analyse the limitation of this representation using the Newton maximization algorithm and we introduce the concept of direction of ambiguity. This concept allows a criterion for choosing the kernels that drive the iteration to minimize the error criterion. The results we present show the improvements of the method over a tracking problem. The target is a small car with a great background similarity.