ed at the occlusion problem of target tracking in machine vision, an occlusion detection mechanism is introduced based on the original Distractor-Aware Tracking (DAT) algorithm framework, and a Detection-DAT (DDAT) algorithm is proposed. First, this mechanism extracts color characteristics of the target, calculates similarities between the target frames through color characteristics, and uses the similarity trends and the threshold values of the differences between the frames to determine whether the target has been occluded during tracking. Second, Naive Bayes and nearest neighbor classifiers are adopted to obtain the target frame in subsequent frames. Finally, similarity is applied to detect whether the target frame obtained by the two classifiers is the correct target frame. To verify the effectiveness of the algorithm, qualitative and quantitative comparisons with the DAT algorithm and other tracking algorithms were performed on the standard data set video sequence with occlusion properties.