Increment subspace target tracking method based on Lp norm regularization

Abstract

The invention discloses an increment subspace target tracking method based on Lp norm regularization. The increment subspace target tracking method comprises the following steps: reading a video sequence image; manually marking an image of a first frame target; acquiring an observation value through utilizing a Ross method; directly calculating residual errors of front five frames of images to determine a target state of the current frame, and collecting a sample; acquiring an initial subspace and a mean vector by adopting the Ross method; establishing a target function for observed samples under an augmented Lagrangian framework from the sixth frame, and carrying out minimization calculation; estimating the confidence coefficient that each candidate sample becomes a current frame real target, and collecting the sample; and when five samples are collected, updating the subspace and the mean vector by carrying out increment through the Ross method. Under the condition of guaranteeing certain instantaneity, the increment subspace target tracking method has relatively strong interference resistance under challenges, such as shielding, light illumination and motion blur, on the target; and the influences on tracking by different sparseness can be observed under one uniform framework.

Description

Technical field: [0001] The invention belongs to the field of machine vision, in particular to a p Norm-Regularized Incremental Subspace Object Tracking Approach. Background technique: [0002] Object tracking in video has important applications in video surveillance, human-computer interaction, behavior analysis and other fields. Although in the past ten years, experts and scholars from various countries have proposed many methods and made a lot of exciting progress in this field, it is still a very challenging task to track the target in real time and robustly. One of the reasons is that the video is a dynamic time sequence, and the posture of the target will change during the travel process, and may encounter interference from lighting, occlusion, and similar objects. At the same time, in the process of traveling, the shaking of the camera or the rapid movement of the target will also cause the appearance of the target to be blurred, which further aggravates the difficu...

AI Technical Summary

Problems solved by technology

[0004] However, the method based on subspace collaborative representation needs to make full use of each feature component in the subspace to reconstruct the target, and inevitably introduce redundant feature components

In addition, the processing of occlusion is still inappropriate. For example, the rectangular template in [2] has the ability to reconstruct the foreground and background at the same time. When each rectangular template is used for collaborative representation, it is bound to further deteriorate the two. meaning, causing the target to deviate or even lose

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