Target tracking method based on inter-frame constraint super-pixel encoding

Abstract

The invention discloses a target tracking method based on inter-frame constraint super-pixel encoding, mainly aiming to solve the tracking problem of failure in tracking due to shielding of a target, rapid motion and deformation in the appearance. The method comprises the following implementation steps: (1) preprocessing a first frame image; (2) constructing an initial dictionary; (3) encoding a sample; (4) training a classifier; (5) encoding a current frame detection sample; (6) predicting a target frame location; (7) updating the dictionary; (8) accurately encoding the sample; (9) upgrading the classifier; (10) judging whether an image is a last frame image, if so, ending, otherwise, returning to the step (5) to process a next frame image. By adopting a super-pixel segmentation and constraint encoding method, the complexity of a subsequent image processing task is lowered, the consistency principle of image spatial information is ensured, the consistency between the local boundary information and texture information of the image is kept, and a target can be tracked stably and accurately.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a target tracking method based on inter-frame constrained superpixel coding in the technical field of computer vision. The invention adopts an inter-frame constraint coding estimation method based on superpixel segmentation to realize moving target tracking, and can be used for supervising video monitoring behavior, analyzing meteorology and forecasting weather. Background technique [0002] The main task of moving object tracking is to detect the moving object to be tracked from the video image sequence, and then determine the position of the moving object in each frame of image. With the continuous deepening of people's understanding of the field of computer vision, object tracking has increasingly become one of the technologies that have attracted much attention in this field. Although there are already a large number of algorithms to achieve moving object track...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the weighted graph model is constructed through the prior shape information and color information of the human body. Although it can solve the tracking in the case of background clutter and target drift, when the target deformation is large, the illumination changes, blur, and occlusion, It is easy to lose the spatial position information of the video image, and it is difficult to achieve accurate and effective tracking

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