SALIENCY PRIOR CONTEXT MODEL FOR VISUAL TRACKING
In this paper, we present a new FFT-based visual tracking algorithm based on a new model for computing prior context distribution via a spectral saliency approach. The tracking problem is formulated under a Bayesian framework where the statistical relationships between the features of the target and its spatio-temporal context are modeled. When building the context model, the prior distribution of the possible target position is an important part worth studying. To deal with various cases of distributions based on different attributes of the target and its context, we exploit low level saliency features by spectral analysis to compute prior distribution, not limited to center-surround weights. We show by extensive experiments that the performance of the new tracking algorithm based on the new saliency prior context (SPC) model achieves real-time computation efficiency with overall best location accuracy performance compared with other state-ofthe- art methods.