This study proposes a multi-modal deep-level high-confidence fusion tracking algorithm in response to the tracking failure issues caused by changes in target appearance and environment in single-target tracking applications.First,a high-dimensional multi-modal model is constructed utilizing the target's color model combined with a shape model based on bilinear interpolation HOG features.Then,candidate targets are searched using particle filtering.The challenge posed by model fusion is addressed by scrupulously quantifying a range of confidences in shape and color models.This is followed by the introduction of a high-confidence fusion criterion,which enables a deeply-adaptive,weighted,and balanced fusion with different confidence levels in the multi-modal model.To counter the issue of static model update parameters,a nonlinear,graded balanced update strategy is designed.Upon testing on the OTB-2015 dataset,this algorithm's average CLE and OS metrics demonstrated superior performance compared to all reference algorithms,with values of 30.57 and 0.609,respectively.Moreover,with an FPS of 15.67,the algorithm fulfills the real-time operation requirements inherent in tracking algorithms under most conditions.Notably,in some common specific scenarios,the accuracy and success rate of the algorithm also outperform the top-tier algorithms in most cases.
维普
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