Visual tracking method based on minimized upper bound error

A technology of visual tracking and upper bound, applied in the field of computer vision, it can solve problems such as inability to achieve performance, and achieve the effect of ensuring optimality and improving stability and reliability.

Active Publication Date: 2011-05-11
OBJECTEYE (BEIJING) TECH CO LTD
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AI Technical Summary

Problems solved by technology

However, simply combining collaborative learning with original online learning methods cannot achieve the best performance

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  • Visual tracking method based on minimized upper bound error
  • Visual tracking method based on minimized upper bound error
  • Visual tracking method based on minimized upper bound error

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Embodiment Construction

[0019] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0020] Please see figure 1 , is a schematic diagram of the overall structure of the online collaborative learning-based visual tracking method for minimizing the upper bound error of the present invention, which includes step S1 target position estimation, step S2 online training sample extraction, step S3 sample visual feature extraction and step S4 tracking The online collaborative learning of the device, the present invention is described in detail below:

[0021] Please see figure 2 The target position estimation module shows:

[0022] Step S1: Use the tracker to estimate the area of ​​the target in the current frame, the target area including the target position and target size.

[0023] By inputting each image a...

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Abstract

The invention discloses a visual tracking method based on a minimized upper bound error, comprising: estimating a target area in the current frame by utilizing a tracker, wherein the target area comprises a target position and a target size; extracting a sample by taking the estimated target area as a reference; extracting two types of visual characteristics of different properties from the extracted sample; cooperatively lifting learning on line according to the two types of extracted visual characteristics of different properties; updating the tracker; during cooperatively lifting learning on line, selecting two types of visual characteristics of different properties by utilizing two parallel lifting algorithms; in each stage of visual characteristic selection, mutually restraining by utilizing cooperative learning; and configuring the optimal sample attribute by utilizing the cooperative learning while lifting tracker performance by the optimal visual characteristic. During on-linelearning, the tracker does not need to input the label information of the sample, and accumulative errors are not brought even if the tracking result is not completely accurate, thereby guaranteeing the stability and reliability of the tracker.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and relates to a visual tracking method for visual monitoring based on minimizing upper bound errors. Background technique [0002] As a cutting-edge computer technology, computer vision technology is widely used in multimedia, video surveillance and artificial intelligence. In computer vision, how to accurately know the position or even the size, direction, and shape of the target in the video image is a basic problem, and it is also a problem to be solved by video tracking technology. Only based on robust video tracking, basic problems such as target positioning and trajectory calibration in computer vision can be solved; only based on robust video tracking, can the analysis of high-level problems such as target recognition and behavior understanding in computer vision be more accurate. wide range of applications. So how to accurately track the target in the video image has always bee...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06T7/20
Inventor 卢汉清王金桥刘荣
Owner OBJECTEYE (BEIJING) TECH CO LTD
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