Method for tracking infrared small target based on sparse representation and transfer limited particle filtering

Abstract

The invention discloses a method for tracking an infrared small target based on sparse representation and transfer limited particle filtering. The method includes the following steps: first, introducing a blocking binary indication vector representing a normal target region and a noise background outlier region, and building a sparse representation model of an infrared small target according to atarget dictionary template; then, extracting a high-frequency region of an infrared image through saliency detection as prior information in the state transition process of a particle filter model, and predicting the state of the target and sampling particles under the limit; and finally, building a particle filter observation model based on target sample particle sparse reconstruction error difference, and estimating and tracking the state of the target according to an online template update strategy. The state estimation ability of random particles is enhanced, and the adaptability and tracking accuracy of particle sparse representation to small and weak moving targets are improved.

Description

technical field [0001] The invention belongs to infrared small target tracking technology, in particular to an infrared small target tracking method based on sparse representation and transfer-limited particle filter. Background technique [0002] The capture and tracking of small infrared targets has been widely used in the fields of infrared precision guidance, infrared remote sensing, space exploration, and navigation. Target tracking has always been one of the research hotspots in the field of computer vision and image processing. However, in some application environments, the imaging system needs to track the target as far as possible from the target. In this case, the target is within the field of view. Often appear in the form of small targets. Due to the influence of energy attenuation and sensor noise, small infrared targets have the characteristics of weak brightness, small area, small number of pixels, and lack of texture information. In addition, when the backg...

AI Technical Summary

Problems solved by technology

However, in practical applications, the number of pixels of infrared small targets is generally only a few to tens of magnitudes, which is far less than that of general targets, so most of the particles fall on non-target areas after being transferred by Gaussian distribution, and obtain extremely low weights, resulting in Very low sampling efficiency

Therefore, during the tracking process, a large amount of computing time is wasted on these invalid particles, and the target is prone to drift and cause tracking errors

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