High-speed correlation filtering tracking method based on a high-confidence updating strategy

Abstract

The invention relates to a high-speed correlation filtering tracking method based on a high-confidence updating strategy. A target positioning module and a high-confidence updating module are respectively designed. In the tracking process, the target positioning module fuses the gray scale, the direction gradient histogram and the color space characteristics, combines with a characteristic dimension reduction method to train a related filter, and achieves the quick positioning of a target center based on a related filtering algorithm. The high-confidence degree updating module designs a high-confidence degree updating strategy by utilizing the response graph obtained by the target positioning module, namely. the highest response value of the response graph and the average peak correlationenergy (Average Peak-to-Correlation Energy, APCE) are calculated, and scale estimation and model updating are carried out only when the two index values meet conditions at the same time, so that redundant scale estimation operation and filter model updating operation which may introduce noise and cause tracking drift under the condition of low confidence are avoided, and complex scenes such as complicated background and shielding are adapted.

Description

technical field [0001] The invention belongs to the field of computer vision and relates to a high-speed correlation filter tracking method based on a high-confidence update strategy. Background technique [0002] In recent years, the research on target tracking methods has gradually shifted from traditional tracking methods such as optical flow, mean shift, sparse representation, and particle filtering to methods based on correlation filtering and deep learning. Among them, although the target tracking method based on deep learning can significantly improve the tracking accuracy, it will greatly affect the calculation speed of the algorithm; while the tracking method based on correlation filtering is more suitable for the Applied in online tracking scenarios with high real-time requirements. [0003] Correlation filter tracking methods usually require filter model updating and scale estimation to adapt to the possible deformation, rotation, and scale changes of the target ...

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Problems solved by technology

[0005] In order to avoid the deficiencies of the prior art, the present invention proposes a high-speed correlation filter tracking method based on a high-confidence update strategy, further improves the tracking speed and robustness on the basis of the existing correlation filter tracking method, and solves the problem of online tracking It is easy to introduce redundancy and lead to model degradation when the background in the scene is complicated and the target is occluded.

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