Neutrosophic similarity measurement-based scale-adaptive visual target tracking method

Abstract

The invention relates to a neutrosophic similarity measurement-based scale-adaptive visual target tracking method. The method comprises the following steps of: selecting a to-be-tracked target area inan initial frame and calculating a target feature histogram and an initial background histogram; carrying out truth, falsity and indeterminacy measurement aiming at target feature attributes and background feature similarity attributes; establishing a neutrosophic weight vector; introducing the neutrosophic weight vector into a mean shift strategy to determine a target area of a current frame; calculating corresponding truth, falsity and indeterminacy measuring values aiming at scale reducing and expanding and determining a scale updating strategy according to cosine similarity measurement; and updating a target background feature histogram. The method disclosed by the invention has the beneficial effects that an extremely efficient mean shift algorithm is adopted, the corresponding neutrosophic measurement calculating amount is small, the weight vector and scale estimating is low in complexity and high in efficiency and the requirements of real-time target tracking are met; and by utilizing a neutrosophic set theory, the tracking performance of a tracking algorithm coping with challenges of complex backgrounds and like is effectively improved through taking the change of trackedtarget features and the similarity of target / background features into account.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a scale-adaptive visual target tracking method based on neutrosophic similarity measure. Background technique [0002] With the development of science and technology and the improvement of the demand for safe city construction, computer vision applications such as video surveillance, video retrieval, intelligent transportation, and automatic driving are playing an increasingly important role in our lives. However, object tracking, as one of the key technologies, remains a challenging problem. [0003] In the field of object tracking, the mean-shift algorithm is widely used in visual object tracking. During the tracking process, the mean shift algorithm determines the current target position by minimizing the distance between the tracked target and the probability density function of the candidate target area. Since the mean shift tracking algorithm uses color histogram ...

AI Technical Summary

Problems solved by technology

On the one hand, the characteristics of the target itself will be affected by changes in the target pose or external environment; in addition, the tracking frame used to determine the target position is also an estimated value obtained based on the target features, and using the target frame to extract target features is also a Uncertain question

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