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Granularity structure analysis and granularity information measurement are important tools to study the complicated information system. Based on the dual hesitant fuzzy relation, this paper proposes the dual hesitant fuzzy granular structure, and discusses three kinds of partial-order relations on the granular structure. At the same time, the nesting relationship between partial order relations is studied. On the basis of dual hesitant fuzzy granular structure, dual hesitant fuzzy information entropy is defined and its related properties are verified. A numerical example is used to illustrate the partial-order relation and the information entropy in dual hesitant fuzzy information system.

The previous target tracking algorithms based on correlation filters have excellent tracking performance. However, when encountering some challenging problems such as fast motion, occlusion, scale variations, motion blur, etc., tracking drift or even tracking failure occurs during the tracking process. Aiming at the above problems, we propose a novel tracking method. On the basis of kernelized correlation filter, a scale adaptive filter is added to adapt to the scale variations of the target during the tracking process. In addition, a feedback mechanism using the average peak-to-correlation energy (APCE) as the judgment criterion is introduced to enable the model to be updated under the premise of high-confidence and avoid tracking model corruption. Experimental results show that our algorithm performs better than traditional correlation filtering algorithms on challenging sequences.

The correlation filters are the core components of most trackers which achieve the excellent results both on accuracy and real-time. However, the previous trackers are prone to drift away from the target when dealing with the challenging situations, e.g. motion blur and fast motion, which leads to the performance of trackers degration. To solve the above problems, in this paper, we propose a novel algorithm which adds the re-detection mechanism to the traditional kernelized correlation filter for judging and verifying the reliability of the detected target before updating the model. We employ the average peak-to-correlation energy to evaluate the confidence level of the candidate position. The experiment results show that proposed algorithm is more accurate and successful than the traditional algorithms.