Moving target tracking method based on improved multi-instance learning algorithm

Abstract

The invention belongs to the field of computer vision and pattern recognition and discloses a moving target tracking method based on an improved multi-example learning algorithm. Firstly, a random measurement matrix is designed according to the compression perception theory. Then a multi-example learning algorithm is used to sample an example in a current tracking result small neighborhood to form a positive package, and at the same time, sampling an example is carried out in a large neighborhood ring to obtain a negative package. For each example, the characteristic of a character target is extracted at an image surface, and the random measurement matrix is utilized to carry out dimensionality reduction on the characteristic. According to the extracted example characteristic, online learning weak classifiers are utilized, and weak classifiers with strong discrimination ability are selected from a weak classification pool to form a strong classifier. Finally, when a new target position is tracked, according to a similarity score of the current tracking result and a target template, the online adaptive adjustment of classifier update degree parameters is carried out. According to the method, a problem that a tracking result in the existing algorithm is easily affected by an illumination change, an attitude change, the interference of a complex background, target fast motion and the like is solved.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and relates to an adaptive target tracking method in a complex environment, in particular to a moving target tracking method based on an improved multi-instance learning algorithm. Background technique [0002] Object tracking refers to finding the position of the object of interest in each frame of the video sequence. Object tracking has important research value and broad development prospects in the fields of intelligent human-computer interaction, video surveillance, and vehicle navigation. At present, many scholars at home and abroad are devoted to the research of target tracking and have made certain achievements. However, the development of target tracking technology is still restricted by factors such as light intensity, weather changes, changes in target appearance, fast movement of targets, occlusion, and complex background interference. [0003] An effective obje...

AI Technical Summary

Problems solved by technology

However, this method ignores some useful information in the tracking process, especially the motion information of the target

At the same time, this method cannot solve the ambiguity problem of target detection itself.

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