Target tracking method based on sparse feature selection

Abstract

The invention relates to a target tracking method based on sparse feature selection. According to the method, firstly, a target, a background and a to-be-selected target point are expressed by use of Haar-like features; then, feature selection is performed on high-dimensional Haar-like features by use of a special nature of sparse expression, and features well distinguishing the target and background are selected as expression of sample points; and finally, selected sample points are used for training a na ve Bayes classifier, and updating classification is performed online, so that the classifier can reflect the relation between the target and background in real time. A projection matrix is constructed by use of the sparse feature selection method, dimensionality reduction is performed on conventional high-dimensional Haar-like features, the calculated quantity is reduced, meanwhile, features helpful to classification are retained, the target and background can be distinguished more effectively, and rapid robustness tracking of the target is realized.

Description

technical field [0001] The invention belongs to computer digital image processing technology, in particular to a target tracking method based on sparse feature selection. Background technique [0002] Target tracking algorithms can be roughly divided into two categories: generative and discriminative. Generative tracking algorithms usually generate an appearance model for the target to be tracked, and find the candidate target with the highest similarity as the tracking result by matching the appearance model. The discriminative tracking algorithm uses a different method, which regards tracking as a binary classification problem, and trains a classifier through positive and negative samples to distinguish the target from the background. [0003] In recent years, the target tracking method based on particle filter has received great attention. Particle filter is a technique for estimating the motion state from noisy data, which approximates the probability by propagating a l...

AI Technical Summary

Problems solved by technology

[0005] In order to avoid the deficiencies of the prior art, the present invention proposes a target tracking method based on sparse feature selection, which solves the problem that the contrast between the target and the background in the image sequence is low, and the gray features are susceptible to noise, illumination changes and interference from similar objects. Impact and other issues

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