Target tracking method for modeling by integrating description method and discriminant method

Abstract

The invention discloses a target tracking method for modeling by integrating a description method and a discriminant method, which comprises the following steps: 1, t=1, randomly extracting a multiscale small image block set from a tracked object and neighboring backgrounds of the tracked object in a first frame image and training a pair of support vector machines in a second category and a pair of support vector machines in a first category serving as a model of the tracked object; 2, randomly extracting a multiscale small image block set in a t+1 frame image and determining degree of confidence that the small image block set in the t+1 frame image belongs to the tracked object through the model of the tracked object, and constructing a confidence image by using the degree of confidence of a small image block in a new frame image and central coordinates of the small image block; acquiring a new position of the tracked object in the new frame image by using a mean shift algorithm on the confidence image; classifying the small blocks in the current frame according to the degree of confidence of the image block and the new position of the tracked object, and updating the model of the tracked object according to the classification result; 3, if not reaching the last frame of a video file, increasing t and returning to the step2; and 4, stopping.

Description

technical field [0001] The invention belongs to the technical field of computer vision and relates to a method for tracking a target Background technique [0002] Target detection and tracking is one of the main research directions of computer vision. It has a wide range of applications in the fields of human-computer interaction, intelligent monitoring, medical image analysis, mobile robot visual navigation, video image analysis and so on. An accurate and robust object tracker will be of great help to subsequent analysis. [0003] Existing target tracking algorithms can usually be divided into two categories in terms of mathematical methods: generative methods and discriminative methods. The generative method learns the appearance of the tracked object and obtains a descriptive model of the appearance of the tracked object. Generative methods adapt to changes in the appearance of the tracked object by updating the descriptive model, while ignoring the influence of the bac...

AI Technical Summary

Problems solved by technology

However, the existing discriminative methods usually only focus on the discriminative model of the target and the background, so it is easy to produce model deviation, and the update method adopted is difficult to reflect the real change of the target, so serious "model drift" often occurs. ” problem, resulting in the loss of the tracked target

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