Moving target tracking method based on KCF and human brain memory mechanism

Abstract

The present invention discloses a moving target tracking method based on the KCF and the human brain memory mechanism. A visual information processing cognitive model based on the human brain memory mechanism is introduced into the target template updating process of the KCF algorithm, and a method based on MTU (Memory-based Template Updating) forms a new model updating strategy, so that each template will be subject to transmission and processing of three spaces of instant memory, short-term memory and long-term memory. In the target tracking process, the target template is updated with different updating strategies according to the matching degree between the target template in the current frame and the target template in the memory space. By effectively remembering the previous scene, the method can keep the robust tracking even when the current target has the sudden change in attitude, reoccurs after temporary disappearance, is sheltered, or the like.

Description

technical field [0001] The invention relates to a method for tracking a moving target in an image sequence, in particular to a method for tracking a moving target based on a kernel correlation filter algorithm (KCF, Kernelized Correlation Filter) and a human brain memory mechanism, and belongs to the technical field of computer vision. Background technique [0002] Moving object tracking is an important research direction in the field of computer vision, which is widely used in intelligent security, visual surveillance, human-computer interaction and other fields. In recent years, although the target tracking technology has made great progress, it is still difficult to achieve accurate target tracking under complex conditions such as illumination changes, target geometric deformation, target occlusion, and fast motion. [0003] At present, the target tracking method based on discrimination has become the mainstream, including: based on structured output tracking with kernel ...

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Problems solved by technology

[0005] On the other hand, although the KCF algorithm has advantages, it also has certain limitations: first, it cannot deal with the problem of complete occlusion of the target; second, it lacks an adaptable model update strategy

Therefore, when the moving target has a sudden change in attitude, the target reappears after a short disappearance, or the target is occluded, the tracking accuracy of the KCF algorithm will drop significantly, or even fail to track.

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