A Weakly Structure-Aware Visual Object Tracking Method Fused with Context Detection

Abstract

The invention discloses a weak structure perception visual target tracking method combined with context detection. During initialization, the model is established by perceiving the weak structural relationship between the target and the components of the surrounding environment. The model maintains two sets of parts corresponding to the target and the surroundings, and uses feature points and feature descriptors to express the appearance of parts. In the tracking process, the component set is combined with the motion model to generate potential target centers, and then through clustering the potential target centers, the noise is eliminated to obtain accurate target positions, and the target size is updated. In the framework of weak structure tracking, in order to enhance the prediction of component locations, two ways of bottom-up and top-down are introduced to detect the target context. Bottom-up probing provides consistent tracking information for components by estimating local motion at the pixel level. Top-down detection learns the difference between the target and the background at the individual level by building a superpixel kernel model, and provides guidance information for target positioning and model updating.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and relates to a target tracking method, more specifically, relates to a weak structure-aware visual target tracking method integrated with context detection. Background technique [0002] Visual target tracking refers to using a continuous video image sequence as input, and for a specific target in it, to determine the position or image area of ​​the target in consecutive frames. As an important research in the field of computer vision, object tracking plays an important role in many intelligent vision systems. Its typical applications include intelligent monitoring, automatic driving and human-computer interaction. [0003] In recent years, many researchers have done a lot of research on visual object tracking, made great progress, and overcome many difficulties in some specific application fields. However, for visual target tracking, it is still a difficult challenge to accurately tr...

AI Technical Summary

Problems solved by technology

However, for visual target tracking, in some complex situations, such as when the target has a large shape change due to fast movement, shape deformation, occlusion, and surrounding environment, it is still a difficult challenge to accurately track the target.

For a generic object tracking method that does not specify the object type, it will be more difficult to track objects of arbitrary classes

[0004] In summary, the existing target tracking methods have limitations in practical use, so it is necessary to improve

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