Compressive perceptual hashing tracking

This paper proposes a novel compressive sensing based perceptual hashing algorithm for visual tracking. A tracking object is represented by dimensionality reduced feature projected from perceptual hashing feature through a sparse measurement matrix. Besides, an updating weight map is assigned for each object and the weight map is updated according to the accumulation of foreground block and the distance between the foreground block and the center of the weight map. Based on above object representation and its weight map, our tracker searches the local region with the maximum similarity in coarse-to-fine way. In addition, we introduce a visual attention knowledge that the object, namely foreground, should be always located in the center of the weight map, to handle the model drift problem. Extensive experiments demonstrate that the proposed tracking method outperforms state-of-the-art methods in challenging scenarios and our tracker is especially insensitive to the location of the initial box.