Target Tracking Algorithm Based on Spatiotemporal Perceptual Correlation Filtering

Abstract

The invention discloses a time-space perception-based correlation filtering target tracking method, which relates to the field of image processing target tracking. This method follows the idea of ​​reading the target image, extracting target features, training filter templates, extracting target multi-scale features, and determining the scale and position in the new image of the target to track the target. The structural framework and target features of the correlation filtering target tracking algorithm The extraction method has been optimized and improved, which makes this method have obvious advantages compared with traditional algorithms in terms of tracking robustness and accuracy. This method can enhance the robustness and accuracy of correlation filtering target tracking, solve the model drift problem caused by linear update of the template, improve the long-term tracking effect of correlation filtering target tracking, and at the same time ensure the real-time performance of target tracking. An important improvement in the.

Description

technical field [0001] The invention relates to the technical field of computer vision and image processing target tracking, in particular to a target tracking algorithm based on spatio-temporal perception correlation filtering. Background technique [0002] With the development of computer vision technology, target tracking technology has gradually become a hot research issue, and it has been more and more widely used in fields such as computer vision and so on. However, target tracking is still a difficult problem due to factors such as target deformation, occlusion and background interference in target tracking. In recent years, correlation filtering target tracking has attracted extensive attention due to its high speed and good robustness. [0003] Foreign scholars have conducted in-depth research in this field: Bolme et al. applied correlation filtering to the field of target tracking for the first time, trained the target correlation filter template by minimizing the...

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Problems solved by technology

[0004] The above algorithm has been innovated and improved for the correlation filter target tracking algorithm, but there are still the following problems: the accuracy and robustness of target tracking need to be further improved; the filter template adopts a linear real-time update strategy, which will cause template drift when the target is occluded , and this update method causes the tracking process to rely more on the most recent samples, so long-term tracking will increase instability

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