Adaptive Object Tracking Based on SNR Maximizing
基于信噪比最大化的目标自适应跟踪

According to the poor tracking ability adopting static feature model, an adaptive feature generating model based tracking program is present. In this program, the object is valid tracking signal, on the contrary, the background is noise constructing the likelihood maps a local SNR(Signal Noise Ratio) is computed to evaluate the tracking ability of current feature space, and the feature space with maximal SNR is selected as the optimal tracking feature space. Object tracking results based on mean shift demonstrated that the proposed method is more robust and feasible than the classical one.