Target tracking method and system based on on-line initialization gradient enhancement regression tree

A target tracking and regression tree technology, applied in image data processing, instruments, calculations, etc., can solve the problem of low accuracy, achieve good results, and solve the effect of changing the appearance of the target

Active Publication Date: 2013-07-31
PEKING UNIV SHENZHEN GRADUATE SCHOOL
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Problems solved by technology

Saffari proposed in the literature A.Saffari, C.Leistner, J.Santner, M.Godec and H.Bischof, ``On-line Random Forests", IEEEIntern

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  • Target tracking method and system based on on-line initialization gradient enhancement regression tree
  • Target tracking method and system based on on-line initialization gradient enhancement regression tree
  • Target tracking method and system based on on-line initialization gradient enhancement regression tree

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Embodiment Construction

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. It should be understood that the described embodiments are only some of the embodiments of the present invention, not all of them. example. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present invention.

[0031] First, the random forest is used for detection, and the residual of the objective function is obtained to initialize the gradient enhanced regression tree, so as to quickly improve the detection results and obtain more robust target tracking results.

[0032] Technical content of the present invention:

[0033] A robust target tracking method based on online initialization gradient enhanced regression tree is proposed, so fur...

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Abstract

The invention relates to a target tracking method and a system based on an on-line initialization gradient enhancement regression tree. In the system consisting of a video input end, a tracking target output end and an on-line training classifier, the method comprises the steps of 1) selecting a tracking target from a video series and extracting positive and negative samples of a Haar-like feature, 2) randomly establishing the on-line classifier according to the positive and negative samples to obtain a training residual error, 3) conducting training amendment by taking the training residual error as a training sample of the on-line classifier and establishing a target model, and 4) acquiring an image confidence map from a next frame of video image, determining a maximum position of a confidence value in a target window, and accomplishing tracking. According to the method and the system, the tree can be converged to an optimum point quickly to ensure that the random forest detection optimization is accomplished, the classifier is updated through on-line study, and the problems of appearance variation, rapid movement and shielding of a target are solved well.

Description

technical field [0001] The invention belongs to the field of target tracking in the field of machine vision and the field of intelligent human-computer interaction, in particular to a robust target tracking method based on online initialization gradient enhanced regression tree, and belongs to the field of target tracking in the field of machine vision and intelligent human-computer interaction. Background technique [0002] Visual object tracking technology is one of the core topics of machine vision research, which integrates key technologies in many fields such as image processing, pattern recognition, artificial intelligence, and automatic control. Visual target tracking technology is widely used in human-computer interaction fields such as video surveillance, intelligent robots, intelligent transportation, and military fields. Because of the huge application prospect, the international and domestic research on visual object tracking is in the ascendant. [0003] In rea...

Claims

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Application Information

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IPC IPC(8): G06T7/20
Inventor 刘宏梁子琳丁润伟
Owner PEKING UNIV SHENZHEN GRADUATE SCHOOL
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