Random projection fern based real-time target tracking algorithm

Abstract

The invention discloses a random projection fern based real-time target tracking algorithm, and relates to machine vision and pattern recognition technologies. The target tracking algorithm comprises four steps of: step 1: an algorithm initialization module; step 2: a random projection fern training module; step 3: a random projection fern classification module; and step 4: a training set updating module. According to the target tracking algorithm provided by the invention, random projection is introduced in addition to random fern, and the target tracking algorithm has the advantages of having the fast calculation speed, few memory demands, being accurate in tracking and easy for transplanting of an embedded system, and therefore can be widely applied in the field of visual navigation, man-machine interaction and monitoring systems and the like.

Description

technical field [0001] The invention relates to computer vision and pattern recognition technology, in particular to a real-time target tracking algorithm based on random projection ferns, which can be applied to the fields of human-computer interaction, video surveillance and the like. Background technique [0002] Object tracking has always been a hot issue in the field of computer vision and pattern recognition. It has a wide range of applications in video surveillance, human-computer interaction, and vehicle navigation. At present, target tracking faces many challenges, such as illumination changes, pose changes, occlusion interference and so on. Current tracking algorithms can be divided into two categories from the target appearance expression mechanism: target tracking algorithms based on generative models and target tracking algorithms based on discriminative models. Among them, the generative model refers to building an adaptive model, that is, learning a represent...

AI Technical Summary

Problems solved by technology

However, random fern also has some defects: First, the gray value comparison of each pixel pair only has two outputs, 0 or 1, so it cannot fully express the target information

Secondly, this method requires a large amount of memory, which is exponentially related to the logarithm of pixels on each fern, so it is unfavorable for embedded system transplantation or some applications that require high frame rates

The improved algorithm and technology for target tracking based on random ferns has been disclosed in the application document with the publication number CN102982340A, but this method does not solve the inherent defects of random ferns, and combines the optical flow method in the tracking process. more memory consumption

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