Abstract：To achieve more efficient utilization of image feature information and improve the tracking accuracy as well as robustness of the target, an improved super pixel tracking algorithm via fusing salient region detection into spatio-temporal context is proposed. Firstly, super pixel segmentation is conducted in the context region of the target, then the motion relevance of target context and the regional covariance information are utilized to calculate the correlation saliency of the image super pixels. Based on Bayesian framework, the model fusing saliency detection into spatio-temporal context is built in the frequency domain. Next, the color and texture histograms of current frame and reference template are employed to calculate the Bhattacharyya coefficient and update the spatial and temporal context model. The scale pyramid model is introduced to estimate the target scale. Finally, the adaptive motion prediction module is incorporated by updating online dynamic model sample set and using ridge regression method to determine the parameters of a low pass filter. Experimental results on public database indicate the superiority of the proposed algorithm over other algorithms in illumination change, complex background, object rotation, high mobility, low resolution.
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2017, Vol. 30 
