Adaptive scale image sequence target tracking method based on feature filtering and fast motion detection template prediction

Abstract

The present invention provides an adaptive scale image sequence target tracking method based on feature filtering and fast motion detection template prediction. The method comprises the following steps of: 1, specifying a to-be-tracked object in an initial frame, transforming the object into a frequency domain by fast Fourier transformation based on a Gaussian kernel, and constructing a feature filter; 2, constructing a fast motion prediction template; 3, constructing an object imaging scale change model; 4, in a second frame, acquiring a predicted position of the object by the fast motion prediction template, acquiring a position of a corresponding matched search box according to the object imaging scale change model by using the predicted position as the center, performing filteration by the feature filter, regarding a position of a maximum response value as an object location, and simultaneously, generating a new feature filter and using the new feature filter as a to-be-used feature filter in a next frame; 5, sequentially repeating the step 4 in subsequent frames until a video or image sequence is finished. The method provided by the present invention is effectively applicable to tracking of a distant and small object in a complicated background and has good tracking performance.

Description

Technical field [0001] The invention relates to the field of video target tracking, in particular to an image sequence target tracking method. Background technique [0002] The camera always keeps track of the target during the process of quickly approaching the target in the long-distance and high-speed motion state, which is one of various target tracking application scenarios. In a long-distance scenario, the image size of the target object on the sensor is very small, often less than 15×15 pixels, or even only 5×5 pixels. However, at such a scale, the characteristics of the imaging area are extremely inconspicuous, which is not conducive to the use of object color, texture, structure and other characteristics for tracking. In a natural scene, the background of the target object is very complicated and random, and the foreground may be blocked. Existing tracking algorithms cannot complete the tracking task well in such application scenarios. The algorithm can track the targ...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of the existing target tracking methods, which are difficult to detect features and have poor tracking performance for long-distance small targets in complex backgrounds, the present invention provides a t

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