A Novel Visual Object Tracking Algorithm Based on Multiple Clues Probability Distribution Image Fusion

In order to overcome shortcoming of fixed clues weight,Different features are used to generate a set of likelihood maps for each pixel indicating the probability of that pixel belonging to foreground object or scene background.The confidence score of each likelihood map is computed based on distinction of histograms of likelihood values on the object versus values from the surrounding background region,measured from the likelihood map of the previous frame.The evidence combination framework dynamically updates the weights such that,in the fused likelihood map,discriminative foreground/background information is preserved while ambiguous information is suppressed.Experimental comparisons demonstrate the proposed method outperform the classical fixed clues Weight fusion technique.