A Saliency Prior Context Model for Real-Time Object Tracking

Real-time object tracking has wide applications in time-critical multimedia processing areas such as motion analysis and human-computer interaction. It remains a hard problem to balance between accuracy and speed. In this paper, we present a fast real-time context-based visual tracking algorithm with a new saliency prior context (SPC) model. Based on the probability formulation, the tracking problem is solved by sequentially maximizing the computed confidence map of target location in each video frame. To handle the various cases of feature distributions generated from different targets and their contexts, we exploit low-level features as well as fast spectral analysis for saliency to build a new prior context model. Then, based on this model and a spatial context model learned online, a confidence map is computed and the target location is estimated. In addition, under this framework, the tracking procedure can be accelerated by the fast Fourier transform. Therefore, the new method generally achieves a real-time running speed. Extensive experiments show that our tracking algorithm based on the proposed SPC model achieves real-time computation efficiency with overall best performance comparing with other state-of-the-art methods.