科研之友
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摘要
Recently, Compressive Tracking (a) method, one of tracking-by-detection methods, has been widely explored in video target tracking because of its high efficiency. However, it cannot well deal with illumination variations due to its limited target representation. To remedy this, we propose an adaptive CT algorithm and it significantly improves conventional CT in four aspects. First, the efficient illumination invariant features extracted on the basis of the Locality Sensitive Histograms are used to represent the appearance of a target, which is robust to illumination changes. Second, the color attributes tracker is adopted to predict the target position for re-building the new weighted discriminant function which brings the color information to make up for the inadequacy of Haar-like characteristics. Third, a new model updating mechanism is proposed to preserve the stable features while avoiding the noisy appearance variations during tracking. Fourth, a trajectory rectification method is employed to refine the tracking location when possible inaccurate tracking occurs. Finally, experimental results conducted on benchmark dataset show that our tracker achieves state-of-the-art performance in a comprehensive evaluation.
