High-precision infrared target tracking method fusing correlation filtering and particle filtering

Abstract

The invention provides a high-precision infrared target tracking method fusing correlation filtering and particle filtering, and the method comprises the steps: introducing an Lp norm into an LRST tracker, and constructing a tracker minimization model Lp-LRST; estimating the position and the scale of a target by using a DSST tracker, and calculating a PSR value to measure the credibility of a tracking result; if the PSR is greater than or equal to a set threshold value, executing an Lp-LRST tracker according to the target position and scale determined by the current frame of the DSST tracker, otherwise, re-determining the target position by the Lp-LRST tracker according to the target state of the previous frame; if the PSR is greater than or equal to a set threshold value, updating the template of the DSST tracker, otherwise, stopping updating; when the template similarity of the particles is lower than a set threshold value, updating the template of the Lp-LRST tracker, otherwise, stopping updating; transmitting the obtained target position and scale to a DSST tracker of the next frame; and repeating the above steps until the tracking is finished. According to the method provided by the invention, the accuracy and robustness of infrared target tracking can be improved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a high-precision infrared target tracking method that combines correlation filtering and particle filtering. Background technique [0002] Infrared target tracking has always been a hot research direction in the field of computer vision, and it has important applications in the fields of precise guidance of infrared target imaging, infrared warning, automatic driving, human-computer interaction, and scene monitoring. Similar to the principle of the visible target tracking algorithm, the infrared target tracking algorithm needs to determine the state of the target in subsequent frames. At present, many infrared target tracking algorithms are derived from visible target tracking algorithms. However, compared with visible target images, infrared target images have defects such as low resolution, low SNR (Signal-to-Noe Ratio), lack of effective color, shape and texture information, wh...

AI Technical Summary

Problems solved by technology

At the same time, the algorithm is very suitable for infrared target tracking, because the tracking performance of the algorithm does not depend on the texture and edge features of the target

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